Keywords: Constrained control
Abstract: This paper presents a spatiotemporal tube (STT)-based control framework for satisfying Signal Temporal Logic (STL) specifications in unknown control-affine systems. We formulate STL constraints as a robust optimization problem (ROP) and recast it as a scenario optimization program (SOP) to construct STTs with formal correctness guarantees. We also propose a closed-form control law that operates independently of the system dynamics, and ensures the system trajectory evolves within the STTs, thereby satisfying the STL specifications. The proposed approach is validated through case studies and comparisons with state-of-the-art methods, demonstrating superior computational efficiency, trajectory quality, and applicability to complex STL tasks.

Keywords: Linear systems, Automata, Optimization
Abstract: In this paper, we present a novel RRT*-based strategy for generating kinodynamically feasible paths that satisfy temporal logic specifications. Our approach integrates a robustness metric for Linear Temporal Logics (LTL) with the system's motion constraints, ensuring that the resulting trajectories are both optimal and executable. We introduce a cost function that recursively computes the robustness of temporal logic specifications while penalizing time and control effort, striking a balance between path feasibility and logical correctness. We validate our approach with simulations and real-world experiments in complex environments, demonstrating its effectiveness in producing robust and practical motion plans. This work represents a significant step towards expanding the applicability of motion planning algorithms to more complex, real-world scenarios.

Keywords: Optimal control, Mechanical systems/robotics, Nonlinear systems
Abstract: This paper presents a controlled-time tracking approach for omnidirectional mobile robots using sliding mode control. Conventional sliding mode control is known for it’s advantageous order-reduction property and the coefficients of the sliding functions are usually chosen from the reduced-order system established on the sliding manifold. However, recent literature reveals that even the full-order system can be used for choosing sliding coefficients even when order-reduction is established. This has been done for planar holonomic systems like marine vehicles, which provides a motivating source for applying similar approaches to control omnidirectional mobile robots given their holonomic kinematics. A sliding mode controller is developed based on an optimal choice of the sliding coefficient using full-order lyapunov matrices. The control design parameters ensure decay of uncertain terms, while ensuring that the error trajectories reach the sliding manifold quickly and maintain smoothness when entering the manifold. The robustness of the closed-loop system is proved via Lyapunov theory. Numerical results support the proposed approach.

Keywords: Learning, Neural networks, Biologically-inspired methods
Abstract: This paper presents an improved periodic autoencoder architecture for learning compact and interpretable representations of locomotion gaits in legged robots. Unlike conventional time-series models that require storing full joint trajectories, our approach exploits the inherent periodicity of locomotion to encode motion patterns using a minimal set of Fourier parameters: frequency, amplitude, offset, and phase difference. The encoder–decoder framework employs 1D convolutional layers to transform high-dimensional joint angle sequences into low-dimensional latent embeddings, which are then decomposed via a differentiable Fast Fourier Transform (FFT) module. A carefully designed multi-term loss function enforces accurate trajectory reconstruction, sinusoidal consistency in the latent space, and stability of periodic parameters within each gait type. Experimental evaluation on the Loco-MuJoCo Unitree H1 dataset demonstrates that our method accurately reconstructs both walking and running gaits while significantly reducing storage requirements. The learned periodic representations effectively capture the coordination dynamics of legged locomotion and provide a compact foundation for efficient motion generation and control.

Keywords: Learning, Intelligent systems, Mechanical systems/robotics
Abstract: Trajectory generation forms a crucial bridge between high-level decision-making and low-level control in robotics, enabling precise, repeatable, and adaptive motion for complex tasks across domains such as medical robotics and industrial automation. Traditional methods, such as dynamic movement primitives (DMP), effectively generate robot trajectories but require careful hyperparameter tuning for optimal performance. While this helps to some extent, the combined approach still struggles with complex, non-linear motion patterns, takes a lot of time to train, and often becomes unstable during long tasks. To address these challenges, a dynamic adaptive trajectory radial network (DATRN) is proposed for real-time trajectory generation in robotic manipulation tasks. This approach leverages radial basis function neural networks (RBFN). Our experiments on four representative tasks, pick & place, stacking, lateral pushing, and surface wiping, demonstrate that DATRN achieves higher reduction in training time, higher trajectory fitting accuracy, and better stabilization on long-horizon trajectories.

Keywords: Learning, Mechanical systems/robotics, Simulation
Abstract: This paper presents a comprehensive benchmarking framework for multi-skill bipedal locomotion learning using deep reinforcement learning with progressive waypoint-based reward shaping. We introduce the Skills–Algorithms–Rewards (SAR) matrix methodology for systematic evaluation of three actor-critic algorithms (DDPG, TD3, SAC) across five locomotion tasks in a 6-DOF underactuated bipedal robot simulation (Biped-5). Our progressive reward shaping strategy transitions from sparse (2 points) to dense (4+ points) waypoint configurations, enabling quantitative analysis of reward density effects on learning performance. Experimental results reveal distinct algorithmic superiority: SAC excels in stability-critical tasks, achieving 2% fall rates and superior energy efficiency (7.3J), while TD3 dominates dynamic locomotion with 4% fall rates and optimal cost of transport (1.21). SAC demonstrates robust waypoint navigation with 94% success rates and minimal deviation (0.21m), maintaining 84% generalization in complex scenarios. DDPG consistently underperforms across all tasks with 24-94% fall rates due to exploration limitations. Learning curves show continued improvement potential beyond 10M training steps. The Biped-5 benchmark suite establishes task-specific algorithmic guidelines and provides a standardized evaluation platform for advancing bipedal locomotion research.

Keywords: Autonomous systems, (Under)water vehicles, Nonlinear systems
Abstract: In underwater navigation, precise trajectory tracking and reliable obstacle avoidance are crucial for the safe and efficient operation of Autonomous Underwater Vehicles (AUVs), especially in cluttered or complex environments. This paper introduces a novel framework SNATUS that integrates Nonlinear Model Predictive Control (NMPC) with Control Barrier Functions (CBF) to address these needs for slender- body AUVs. These AUVs face unique challenges due to under- actuation and actuator limitations. To ensure smooth control transitions and respect actuator constraints, a regularization term is added to the NMPC cost function that helps avoid abrupt changes in control actions. Safe navigation around obstacles is further enhanced through higher-order CBFs, providing a robust barrier against potential collisions. Additionally, ocean currents are introduced as external disturbances to test the robustness of the proposed approach under real-world conditions. Through a series of simulations, SNATUS architecture demonstrates the AUV’s capability to handle these disturbances, avoid obstacles effectively, and stay on course, proving its effectiveness for reliable underwater navigation.

Keywords: Autonomous systems, Control applications, Neural networks
Abstract: Autonomous ground vehicles (AGVs) must navigate safely in cluttered environments while accounting for complex dynamics and environmental uncertainty. Hamilton-Jacobi Reachability (HJR) offers formal safety guarantees through the computation of forward and backward reachable sets, but its application is hindered by poor scalability in environments with numerous obstacles. In this paper, we present a novel framework called NeuroHJR that leverages Physics-Informed Neural Networks (PINNs) to approximate the HJR solution for real-time obstacle avoidance. By embedding system dynamics and safety constraints directly into the neural network loss function, our method bypasses the need for grid-based discretization and enables efficient estimation of reachable sets in continuous state spaces. We demonstrate the effectiveness of our approach through simulation results in densely cluttered scenarios, showing that it achieves safety performance comparable to that of classical HJR solvers while significantly reducing the computational cost. This work provides a new step toward real-time, scalable deployment of reachability-based obstacle avoidance in robotics.

Keywords: Constrained control, Nonlinear systems
Abstract: In this work we address the issue of controller synthesis for a control-affine nonlinear system to meet prescribed time reach-avoid-stay specifications. Our goal is to improve upon previous methods based on spatiotemporal tubes (STTs) by eliminating the need for circumvent functions, which often lead to abrupt tube modifications and high control effort. We propose an adaptive framework that constructs smooth STTs around static unsafe sets, enabling continuous avoidance while guiding the system toward the target within the prescribed time. A closed-form, approximation-free control law is derived to ensure the system trajectory remains within the tube and satisfies the RAS task. The effectiveness of the proposed approach is demonstrated through a case study, showing a significant reduction in control effort compared to prior methods.

Keywords: Autonomous systems, Constrained control, Optimal control
Abstract: Ensuring both performance and safety is critical for autonomous systems operating in real-world environments. While safety filters such as Control Barrier Functions (CBFs) enforce constraints by modifying nominal controllers in real time, they can become overly conservative when the nominal policy lacks safety awareness. Conversely, solving State-Constrained Optimal Control Problems (SC-OCPs) via dynamic programming offers formal guarantees but is intractable in high-dimensional systems. In this work, we propose a novel two-stage framework that combines gradient-based Model Predictive Control (MPC) with CBF-based safety filtering for co-optimizing safety and performance. In the first stage, we relax safety constraints as penalties in the cost function, enabling fast optimization via gradient-based methods. This step improves scalability and avoids feasibility issues associated with hard constraints. In the second stage, we modify the resulting controller using a CBF-based Quadratic Program (CBF-QP), which enforces hard safety constraints with minimal deviation from the reference. Our approach yields controllers that are both performant and provably safe. We validate the proposed framework on two case studies, showcasing its ability to synthesize scalable, safe, and high-performance controllers for complex, high-dimensional autonomous systems.

Keywords: Vision-based control, Neural networks, Robust control
Abstract: Synthesising safe controllers from visual data typically requires extensive supervised labelling of safety-critical data, which is often impractical in real-world settings. Recent advances in world models enable reliable prediction in latent spaces, opening new avenues for scalable and data-efficient safe control. We introduce a semi-supervised framework that leverages control barrier certificates (CBCs) learned in the latent space of a world model to synthesise safe visuomotor policies. Our approach jointly learns a neural barrier function and a safe controller using limited labelled data, while exploiting the predictive power of modern vision transformers for latent dynamics modelling.

Keywords: Multivehicle systems, Nonholonomic systems, Mechanical systems/robotics
Abstract: The concept of collision cones (CCs) is adopted to generate collision avoidance (CA) maneuvers for non-holonomic robots that can change both speed and direction and that move in the 2-D plane. In the literature, CC-based maneuvers are applied by only one member (robot) of the pair of entities (robot-obstacle) that are on a collision path. Reciprocity in CA is demonstrated for a pair of robots, that are on a collision path, by determining their acceleration inputs such that a CA condition derived using the idea of CCs is satisfied independently by each robot; the inputs are derived in terms of relative velocities in polar coordinates. As is shown, since the single CA condition is a function of the relative velocities, which are the same for both robots, reciprocity occurs naturally and further, reciprocal dances are also avoided. The CC idea is extended to the multi-robot case, where CA maneuvers are performed by that pair of robots whose collision is “most imminent”; the measure of “imminence” is the value of the CA condition function calculated for a pair of robots. Simulation results are presented using kinematics-based dynamics and assuming that each robot is bounded by a circle of known radius.

Keywords: Control applications, Process control, Optimization
Abstract: In this research article, tuning strategies for proportional-resonant (PR) controller with three tuning parameters have been proposed. Initially, all the three parameters of controller are tuned using Particle Swarm Optimization (PSO) algorithm separately. Furthermore, to achieve additional performance achievement, two parameters are retuned while keeping Kp fixed as obtained from the PSO algorithm. This refinement is carried out using the all stability region (ASR) method, which is a graphical tuning technique that utilizes the concept of root crossing boundaries. Controller settings are obtained by computing the centroid of triangle inscribed in all stability region in the plane of controller parameters for stable plants with and without time delay. Obtained results demonstrate that idea of retuning based on combining PSO algorithm with all stability region method significantly enhances the performance of system as compared to recently reported strategies. The usefulness of the proposed tuning strategies is demonstrated by simulation study conducted on different classes of stable processes.

Keywords: Control applications, Spacecraft control, PID control
Abstract: India’s Chandrayaan-3 mission achieved a historic milestone with its successful soft landing near the lunar south pole, highlighting the critical role of the navigation, guidance, and control (NGC) system. Navigation provided vehicle state estimates relative to the Moon’s center, while a polynomial-based guidance scheme computed the required acceleration profile to meet terminal landing conditions. This acceleration demand was translated into total thrust magnitude and attitude commands generation. Attitude command generation involved aligning the thrust axis with the required acceleration vector and constraining rotation about the thrust axis, typically governed by mission-specific requirements. Although quaternion-based control laws are preferred for their singularity-free representation, they inherently couple all three rotational axes. This coupling can lead to undesirable interactions between guidance and control, especially during large rotations about the thrust axis, due to the quaternion’s shortest-path property. This paper proposes a novel quaternion-based decoupling method that enables independent thrust-axis control, mitigating guidance-control interaction and ensuring proper attitude commands generation for lander attitude control.

Keywords: Chaotic systems, Control applications, Nonlinear systems
Abstract: Chaotic systems, characterized by their deterministic nonlinear dynamical equations, exhibit sensitive dependence on initial conditions and a bounded, long-term aperiodic behavior. Thus, making them suitable for secure communications and nonlinear signal processing. This paper presents the FPGA-based hardware implementation of the well-known chaotic Lorenz system and explores various synchronizations between identical master–slave configurations, including complete synchronization, anti-synchronization, and projective synchronization. Furthermore, a new concept of composite synchronization is proposed and realized in hardware, wherein the first state achieves complete synchronization, the second state is in anti-synchronization, and the third has projective synchronization, simultaneously. The entire architecture is synthesized and deployed on a Nexys A7-100T FPGA board using Verilog, and the system dynamics are visualized in real-time in an oscilloscope. Experimental results validate the working of all synchronization modes, including the proposed composite synchronization. The feasibility of implementing in real-time on hardware platforms is thus successfully demonstrated.

Keywords: Control applications, Flight control, Spacecraft control
Abstract: Momentum management is a critical function in spacecraft attitude control systems that utilize reaction wheels, particularly to prevent wheel saturation caused by the accumulation of angular momentum over time. Momentum management refers to the control strategy used to maintain reaction wheel speeds within specified operational limits. Conventional momentum management methods using magnetic torquers often introduce undesirable attitude disturbances, which are typically corrected through feedback control using the reaction wheels themselves. Although this feedback may direct the momentum change as intended, it can still lead to significant attitude errors—particularly detrimental for high-precision pointing missions. Moreover, traditional desaturation strategies do not guarantee proper wheel speed regulation in systems with redundant reaction wheel configurations. This paper presents a generalized framework for reaction wheel momentum management that minimizes attitude disturbance during desaturation and ensures adherence to wheel speed limits even in redundant configurations. The proposed method first computes the total desaturation torque required for momentum unloading. This torque is then decomposed into two orthogonal components: a null-space torque, which redistributes momentum among reaction wheels without affecting the spacecraft's attitude, and a range-space torque, which reduces the spacecraft’s net angular momentum. The range-space torque implemented using magnetic

Keywords: Spacecraft control, Modeling and simulation, Control applications
Abstract: Modern scientific exploration and Earth observation spacecraft missions across the globe have necessitated the assembly of large space structures in space. The urge for the design and development of such assembled spacecraft has increased all the more with the proposals of Indian space station and journey of heavy spacecraft to lunar orbits and outer space for interplanetary missions. This eventually requires attitude stabilization and control of the composite docked spacecraft through the controllers from primarily one module. However, the control authority of reaction wheel-based control from one module towards composite stack control falls short due to high composite inertia and poses challenge to the slew time and other mission requirements. The current paper brings to light the scope of development of control algorithms and strategies for reaction wheel-based control of the composite stack during operation of the in-orbit large space structure in multi-module docked condition towards achieving greater mission goals. The formulations for such spacecraft control in normal mode with reaction wheels are classified into masterslave configuration-based control and Momentum augmented control of coupled spacecraft dynamics with joint participation of both system actuators.Such algorithms widen the horizon of coupled dynamics control and expand the spectrum of multi-stack on-orbit spacecraft operation without transportation of actuation signal across systems.

Keywords: Biomedical, Nonlinear systems, Robust control
Abstract: Robotic-assisted needle steering offers significant potential to improve the precision and effectiveness of needle based medical interventions, including biopsy, brachytherapy, and targeted drug delivery. Variations in tissue properties, internal deformations, and suboptimal guidance strategies can significantly impair the accuracy of target localization by needling system. In percutaneous interventions, the primary goal is to ensure both the safety of the patient and the precise positioning of the needle at the intended target site. In this regard, an autonomous needle steering control framework based on Recursive Terminal Sliding Mode Controller (RTSMC), is developed to guide the needle inside the tissue region. The designed controller incorporates a non-holonomic constraint based differential kinematic model of needle during intervention inside tissue region. The performance of the proposed needle steering control scheme is assessed through simulation studies, which confirm the system’s stability and ability to accurately converges to a desired plane within a finite insertion depth.

Keywords: Predictive control for linear systems, Uncertain systems, Observers for linear systems
Abstract: This article is an abridged version of our paper with the same title, in which we propose an adaptive output feedback model predictive control (MPC) framework for uncertain systems subject to external disturbances. In the absence of exact knowledge about the plant parameters and complete state measurements, the MPC optimization problem is reformulated in terms of their estimates derived from a suitably designed robust adaptive observer. The MPC routine returns a homothetic tube for the state estimate trajectory. Sets that characterize the state estimation errors are then added to the homothetic tube sections, resulting in a larger tube containing the true state trajectory. The two-tier tube architecture provides robustness to uncertainties due to imperfect parameter knowledge, external disturbances, and incomplete state information. Additionally, recursive feasibility and robust exponential stability are guaranteed and validated using a numerical example.

Keywords: Optimal control, Emerging control theory, Randomized algorithms
Abstract: Trajectory-sampling-based techniques, such as the model predictive path integral control (MPPI), are well suited to solve complex optimization problems with numerous weighted cost terms and non-smooth constraints. However, it can be challenging to design the input-sampling distribution to fully capture and correct the effects of system dynamics to enable precision output tracking. This challenge can be resolved with the output-sampled MPPI (oMPPI) approach, which first augments the system with its inverse to generate inputs that account for the system dynamics and achieve accurate output tracking, and second applies the standard MPPI approach to the augmented system with the inverse. The main contribution of this work is to show that optimality of MPPI is preserved with the oMPPI formulation when sampling the system’s reference outputs --rather than sampling the inputs as in standard MPPI. This theoretical development validates experimental improvements shown in prior work on oMPPI.

Keywords: Machine learning, Nonlinear systems, Predictive control for nonlinear systems
Abstract: Directive control of general nonlinear frameworks remains a persistent challenge in modern control engineering, particularly when such systems exhibit mode dependent dynamics or operate under varying external conditions. Model Predictive Control (MPC) is widely used for its ability to handle constraints and anticipate future behavior, but its effectiveness depends heavily on the accuracy and adaptability of the prediction model. Traditional MPC approaches typically rely on linearized or simplified models, which might not fully represent the intricate, nonlinear traits associated with real world systems. To overcome these limitations, this work presents a deep learning based MPC framework that employs a Long Short Term Memory network with attention mechanism for system prediction. The attention mechanism enhances temporal feature learning, enabling the model to autonomously adapt to different dynamic regimes. This predictive model is integrated within an MPC loop, utilizing an adaptive gradient descent approach to effectively tackle the underlying optimization problem efficiently while adhering to system constraints. The proposed method is compared with both conventional MPC and with other Deep Learning based MPC techniques. The approach is experimentally validated on a hardware implementation using a Magnetic Levitation system, demonstrates improved tracking performance and reduced overshoot.

Keywords: Robust control, Predictive control for nonlinear systems, Nonlinear systems
Abstract: Deep Model Predictive Control (Deep MPC) is an evolving field that integrates model predictive control and deep learning. This manuscript is focused on a particular approach, which employs deep neural network in the loop with MPC. This class of approaches distributes control authority between a neural network and an MPC controller, in such a way that the neural network learns the model uncertainties while the MPC handles constraints. The approach is appealing because training data collected while the system is in operation can be used to fine-tune the neural network, and MPC prevents unsafe behav- ior during those learning transients. This manuscript explains implementation challenges of Deep MPC, algorithmic way to distribute control authority and argues that a poor choice in distributing control authority may lead to poor performance. A reason of poor performance is explained through a numerical experiment on a four-wheeled skid-steer dynamics.

Keywords: Process control, Switched systems, Predictive control for linear systems
Abstract: This work presents a finite-time model predictive control (MPC) framework for stabilizing discrete-time switched systems under average dwell-time (ADT) switching. The main idea is to exploit the finite-horizon optimal control design in combination with ADT to guarantee stability even when the system switches between stable and unstable modes. In the unconstrained case, we prove that the system state converges exactly to the origin in a finite number of steps equal to the state dimension. For constrained systems, we establish recursive feasibility and asymptotic stability and show that finite-time convergence is recovered once the state enters an invariant terminal set where the constraints are inactive. A simulation study involving both stable and unstable subsystems demonstrates the effectiveness of the proposed MPC-based approach in comparison with state feedback control.

Keywords: Predictive control for linear systems, Autonomous systems, Control applications
Abstract: This paper presents a comprehensive implementation of path planning and feedback-based path tracking control strategies for Quanser QBot Platform, a differential-drive wheeled mobile robot. A series of 2D LiDAR scans are used to generate probabilistic occupancy maps of the environment with static obstacles. RRT* path planning algorithm is used to generate paths that circumvent any static obstacles present in the occupancy map, given the initial and final positions. Two reference paths considering different terminal points are generated for the path tracking problem. Pure Pursuit and Linear Model Predictive Control (LMPC) based path tracking controllers are experimentally implemented on QBot and achieved remarkable performance accuracy in real-time settings, i.e, MATLAB Simulink. The performance of Pure Pursuit and LMPC is compared based on state errors and form errors.

Keywords: PID control, Control applications, Decentralized control
Abstract: Designing a well-tuned controller for a highly interacting multivariable process is always challenging due to strong interaction effects and significant process dead-time dynamics. The severity of these interactions (disturbance) and time delays is even greater for high-dimensional multivariable processes. To address these issues, a dead-time compensation (DTC) based inverted decoupler framework is proposed in this manuscript. In the proposed approach, the dead-time dynamics of the multivariable process is explicitly taken into account when designing PI controllers. The proposed dead-time compensation scheme eliminates significant time delays in process dynamics and reduces the impact of interaction. Due to the DTC the realization of the inverted decoupler is simplified. The combination of the DTC and the inverted decoupler can facilitate a significant improvement in the transient response of the system when properly tuned controllers are used. Thus, controller effort can be minimized, and a better controller design can be achieved for any multivariable system . Simple IMC PI tuning rules are utilized to obtain controller parameters for each loop. To assess the effectiveness of the proposed method, case studies were carried out on a two-input two-output highly interacting process and a couple of highly interacting high-dimensional multivariate processes. The simulation study confirms that the proposed method provides better closed-loop performance.

Keywords: Linear systems, Algebraic/geometric methods, Emerging control theory
Abstract: In the study of supersymmetry, we encounter systems whose dynamics are represented using both commuting and anticommuting variables. Thus, the state-space of those dynamical systems becomes a supermanifold. Though the dynamical systems emerging from natural phenomena are non-linear in nature, linear control systems play a vital role in studying the systems locally. Therefore, we study the linear control system on Lie supergroups along with its controllability criterion. Moreover, we prove our results using Lie supergroups SL(m|n) and OSp(m|n) of lower dimensions.

Keywords: Reduced order modeling, Large scale systems, Linear systems
Abstract: This paper presents a novel frequency-weighted singular perturbation approximation-based model reduction technique which is implemented for the design of reduced-order two-dimensional (2-D) separable denominator-type discrete-time infinite impulse response (IIR) filters. The proposed technique uses a singularly perturbed balanced realisation that can be applied with single- and double-sided weights. A numerical example of a (12,12) order low-pass 2-D Butterworth filter is examined to establish the effectiveness of the proposed method.

Keywords: Computational methods, Delay systems, Uncertain systems
Abstract: Computer controlled and networked control systems are often modeled as delay difference equations (DDE). Ensuring stability through appropriate controller design for such systems is a challenging task. As has been shown in multiple works, set invariance and classical stability theory have close links. A popular set invariance concept for DDEs is the mathscr{D}-invariance condition. In this paper, we propose mathscr{D}-invariance conditions by making use of zonotopes as set representations. Compared with polytopes, zonotopes provide a compact representation and favorable properties under various set operations, like Minkowski addition. Our mathscr{D}-invariance conditions using zonotopes not only allow for testing feasibility of a given set, but also allows for efficient computation of invariant sets in a particular template. We also use our zonotopic mathscr{D}-invariance conditions to formulate robust mathscr{D}-invariance conditions and a computationally efficient controller design algorithm that optimizes simultaneously the rate of convergence and the amount of control energy used by a candidate controller. Finally, we show the efficacy of our results using numerical examples.

Keywords: Numerical algorithms, Computational methods, Delay systems
Abstract: In this paper, we investigate the concept of cyclic mathscr{D}-invariance for discrete-time linear systems affected by delay. By leveraging zonotopes, a class of polytopes known for their compact representation and favorable properties under Minkowski sums and linear transformations, we develop an efficient framework for computing and analyzing less conservative invariant structures (cyclic invariance) in delayed systems. In this paper, we derive the algebraic conditions under which a cyclic sequence of zonotopic sets remains invariant and extend the analysis to systems affected by bounded disturbances. In addition, we propose a linear program formulation for controller synthesis that ensures the sequence of zonotopic sets is cyclically lambda-mathscr{D}-contractive, ensuring desirable closed-loop performance. The proposed zonotope-based cyclic invariant sets computation offers significant advantages in computational complexity over polyhedral sets and ellipsoidal set representation as complexity increases due to set operations like the Minkowski sum, providing a scalable and efficient method for robust analysis of time-delay systems. Finally, numerical examples are provided to show the effectiveness of the proposed approach.

Keywords: Reduced order modeling, Large scale systems, Modeling and simulation
Abstract: This study presents a novel configuration of frequency-limited controllability and observability Gramians to establish a new singular perturbation approximation-based model reduction approach. The proposed approach minimizes errors within designated frequency ranges for given higher-order models. The proposed method produces stable, simpler models and offers improved approximations compared to current methodologies. Two numerical examples are employed to evaluate the efficacy of the presented method.

Keywords: Agents-based systems, Networked control systems, Observers for linear systems
Abstract: This extended abstract summarizes the key contributions of our recent publication in IEEE Control Systems Letters, which presents a novel emph{event-triggered control} (ETC) methodology for emph{leader-centric time-varying formation tracking (LCTVFT)} in linear multi-agent systems (MASs) subject to actuator bias faults. Unlike prior works that rely on pre-defined formation patterns, our framework allows the leader to dynamically determine formations at runtime. This requires followers to infer and adapt to the evolving formation under constrained computational resources. To overcome the challenge of continuous updates, a fixed-time emph{event-triggered formation observer} (ETFO) is designed, enabling followers to asynchronously estimate leader-driven formation information with minimal updates. Rigorous Lyapunov-based stability analysis demonstrates that the overall formation tracking error converges to zero within a fixed time for each leader-assigned formation interval. A simulation study involving switching between circular and V-shaped formations validates the effectiveness of the proposed approach, highlighting the quantitative benefits of event-triggered updates while maintaining accurate formation tracking.

Keywords: Optimal control, Iterative learning control, Networked control systems
Abstract: In this paper, we present an active learning-based control method for discrete-time linear systems with unknown parameters under denial-of-service (DoS) attacks. For any DoS duration parameter, using switching systems theory and adaptive dynamic programming, an active learning-based control technique is developed. A critical DoS average dwell-time is learned from online input-state data, guaranteeing stability of the equilibrium point of the closed-loop system in the presence of DoS attacks with average dwell-time greater than or equal to the critical DoS average dwell-time. The effectiveness of the proposed methodology is illustrated via a numerical example.

Keywords: Control of networks, Biologically-inspired methods, Modeling and simulation
Abstract: Synchronization in biological systems is a significant concept that facilitates group decision-making and leads to meaningful outcomes. However, the precise conditions required for synchronization in these complex biological systems are not well understood. This study aims to establish sufficient conditions for the synchronization of benchmark biological systems by using their dynamical equations and a contraction theory approach. The systems examined in this paper include the Van der Pol os- cillator, Fitzhugh-Nagumo model, Hindmarsh-Rose model, Good- win model, and Repressilator. These nonlinear oscillators are essential for understanding biological rhythms and play a crucial role in physiological processes. In this research, we couple two identical systems with a virtual system and compute the Jacobian of the virtual system. We then calculate the matrix measure of the Jacobian, aiming to prove that it is negative. This yields sufficient coupling gain for the synchronization of the two identical systems, ensuring exponential convergence. Numerical simulations have been conducted to validate the findings. The results of this study should enhance the understanding of synthetic biological systems and aid in their design.

Keywords: Networked control systems, Output feedback, LMIs
Abstract: In consistent with the need of resource-efficient, this paper proposes an improved adaptive event-triggered mechanism (ETM) for networked control systems under a communication channel. The study develops a output-based adaptive event-triggered sensor transmission for a fuzzy network control model to reduce unnecessary data transmissions. This improved adaptive event-triggered transmission technique minimizes the utilization of constrained network resources. Lyapunov theory is applied to formulate linear matrix inequality (LMI) based stabilization conditions that guarantee the system's stability. The proposed control approach, as demonstrated in the simulations, significantly reduces network resource usage compared to the widely used event-triggered approach.

Keywords: Neural networks, Stability of nonlinear systems, LMIs
Abstract: This article examines the finite-time synchronization and stability of fractional-order neural networks (FONNs) with time-varying delays. The master-slave FONNs are constructed, and the event-triggered control (ETC) mechanism is implemented in the slave FONNs as a control input. Moreover, the proposed ETC has been taken in the form of an input-delay approach. The contribution of this work is to achieve finite-time synchronization and stability for the constructed FONNs under the consideration mentioned above. Moreover, an explicit formula is given to calculate the upper bound of the settling time. Finally two numerical examples is provided to demonstrate the efficiency of the proposed result.

Keywords: Networked control systems, Optimization, Neural networks
Abstract: A cyber-physical system connects the physical and digital worlds by leveraging communication technology in-order to monitor and control the physical systems. One of the major challenges faced by this technology is its vulnerability towards malicious cyber attacks. This work delves into an optimal non-linear deception attack on remote state estimation where a smart sensor node sends its data over a wireless channel to a remote estimator. In-order to avoid the complications while solving an optimization problem that yields the closed form expression of a non-linear function, the attack model is approximated with the help of radial basis function neural network. This approximation is achieved with the aid of an adaptive online weight update method that will produce the approximation of an optimal non-linear attack strategy.

Keywords: Flexible structures, Control applications, Mechanical systems/robotics
Abstract: This work considers human–robot collaborative transportation (co-transport) of flexible objects using force/torque measurement at the robot end-effector, which is standard in industrial robots. The main challenge is that standard admittance control, typically used for rigid-object co-transport can lead to low gain margins and deformation of the object — large deformations can damage flexible objects. The main contribution of this work is to increase the gain margin of admittance controllers by compensating for the flexible-object dynamics and thereby reduce the structural deformation during co-transport. Experimental results with the proposed approach show that the deformation can be reduced by approximately half (45%) when compared to standard admittance control.

Keywords: Mechanical systems/robotics
Abstract: The compliance and agility of soft robots make them ideal for complex tasks in unstructured environments. This paper presents a robust task-space bilateral teleoperation framework for heterogeneous systems, featuring a non-redundant rigid leader and a redundant spatial soft follower, modeled using the piecewise constant curvature assumption. Using passivity-based robust control, we prove ultimate boundedness of system trajectories under parameter uncertainties and constant asymmetric time delays. Additionally, the follower's null-space velocity is leveraged for collision avoidance. Simulation results on a spatial soft robot demonstrate the effectiveness of the proposed framework.

Keywords: Mechatronics, PID control, Linear systems
Abstract: This paper employs a two-loop proportional-derivative (PD) controller to compensate a two-link flexible manipulator (TLFM). For this purpose, first, the transfer function model of this system is obtained by linearizing its (assumed mode-based) nonlinear model available in literature. Then, for this transfer function model, an iterative algorithm based on root-locus study is developed to design the PD controller in order to achieve a specified settling time and H-infinity norms of mixed sensitivity and control sensitivity transfer functions. The performance and robustness of the proposed compensation are verified through extensive simulations of the nonlinear system. The results are also compared with the mixed sensitivity based two-loop and centralized H-infinity controllers.

Keywords: Autonomous systems, Robust control, Flight control
Abstract: This paper presents a modeling and control framework for a dual-tilt birotor unmanned aerial vehicle (UAV). This UAV is equipped with rotors that can tilt about two independent axes. The dual-axis tilt mechanism significantly improves vehicle maneuverability and control authority compared to conventional birotor configurations. Taking advantage of the additional degrees of actuation offered by these tilting rotors, the UAV can independently control both its position and orientation. However, the coupling between rotor speeds and tilt angles introduces nonlinearities in the mapping between control inputs and the resulting forces and moments. To address this, a constant control allocation matrix is employed to approximately map the desired force and torque commands to actuator inputs. To compensate for the mismatch between the actual and assumed control allocation, a robust backstepping control architecture is developed, ensuring system stability and tracking performance in the presence of bounded unmodeled dynamics. Numerical simulations are performed to validate the proposed control strategy in various flight scenarios, including setpoint and trajectory tracking.

Keywords: Control applications, Robust control, Flight control
Abstract: Perching maneuver enables quadrotor to make stable contact with vertical surfaces for prolonged monitoring, significantly enhancing mission endurance and energy efficiency in inspection and surveillance tasks. This work presents the development of an adaptive second-order continuous control (ASOCC) strategy for the perching application of a quadrotor operating under disturbances and model uncertainties. A linear sliding surface guarantees finite-time convergence of the tracking error. A new finite-time convergent disturbance observer is proposed to estimate and compensate for unknown but bounded uncertainties in the system model. The closed-loop stability of the proposed controller, including the observer dynamics, is established through Lyapunov stability theory. Simulation study validates the effectiveness of the proposed approach. A comparative analysis with the standard SOCC method demonstrates the improved performance, robustness, and disturbance rejection capability of the ASOCC strategy.

Keywords: Autonomous systems, Flight control, Modeling and simulation
Abstract: This paper presents the dynamic modeling and control design for an H-configuration quadrotor equipped with folding arms. This morphing mechanism enables decoupled lateral motion and roll angle control, which is not achievable in a conventional quadrotor. The proposed control architecture employs a two-loop orientation control and a single-loop position control using PID controllers. A constant control allocation matrix is used to map the desired control forces and moments to actuator inputs. This approach enhances computational efficiency by allowing the allocation matrix to be hardcoded into the controller. MATLAB simulations are carried out to validate the proposed control scheme and demonstrate accurate tracking of all five degrees of freedom.

Keywords: Constrained control
Abstract: This paper addresses controller synthesis for unknown multi-input-multi-output systems to satisfy temporal reach-avoid-stay (T-RAS) specifications using spatiotemporal tubes (STTs). To design a valid STT, the tube constraints are first formulated as a robust optimization problem (ROP) and then relaxed through a sampling-based scenario optimization program (SOP). Subsequently, an approximation-free, closed-form controller is designed to satisfy the T-RAS specification. Finally, the approach is validated on multiple case studies.

Keywords: Autonomous systems, Automotive, Constrained control
Abstract: This paper proposes a teaching learning based optimization (TLBO) equipped model predictive control (MPC) strategy for simultaneously ensuring safety and passenger comfort in autonomous vehicles (AV) while executing desired lane changing maneuvers. Safe autonomous driving is guaranteed when the vehicle does not exceed its operational limits, whereas passenger comfort is tied to the steering input and its rate of change, which in turn are considered to be the control inputs in the vehicle model. Therefore, the safety of the AV is handled by imposing suitable constraints on the vehicle states, while passenger comfort is taken care of by imposing constraints on the magnitude as well as the rate of change of control inputs. The efficacy of the proposed controller is demonstrated through a simulation experiment. The results show efficient performance of the proposed TLBO equipped MPC in ensuring safety and passenger comfort, through successful constraint satisfaction, and at the same time delivering the desired lane changing performance. The simulation experiment further shows the improvement in the closed-loop performance when the MPC uses the TLBO optimizer instead of an off-the-shelf optimizer.

Keywords: Aerospace, Optimal control, Optimization
Abstract: Coordinating multiple unmanned aerial vehicles (UAVs) in obstacle-rich environments is critical for applications such as surveillance, mapping, and delivery. Achieving energy-efficient and collision-free trajectories is challenging due to inter-UAV conflicts and the presence of obstacles. This work presents an optimal control framework for multi-UAV trajectory optimization based on the differential flatness property of a 3-DOF fixed-wing UAV model.

The problem is formulated as a nonlinear program (NLP) with UAV positions serving as decision variables. A pseudospectral method is employed to approximate the derivatives in the dynamic equations at collocation points. Auxiliary variables such as velocity, heading, and elevation angles, along with control inputs including bank angle, lift coefficient, and thrust, are recovered via flatness-based mapping. Feasibility is ensured by enforcing dynamic and kinematic constraints, while obstacle and inter-UAV avoidance are incorporated as soft penalties.

Demonstrated in point-to-point navigation and formation flight scenarios with obstacles, the proposed method generates safe, dynamically feasible, and energy-optimal trajectories. Trajectory accuracy is further validated through RK45 time integration of the dynamics using interpolated control inputs. The resulting trajectories can be directly utilized as waypoints for high-level guidance, or the recovered control inputs can serve as references for low-level trajectory tracking.

Keywords: Autonomous systems, Constrained control, Neural networks
Abstract: Among the promising approaches to enforce safety in control systems, learning Control Barrier Functions (CBFs) from expert demonstrations has emerged as an effective strategy. However, a critical challenge remains: verifying that the learned CBFs truly enforce safety across the entire state space. This is especially difficult when CBF is represented using neural networks (NCBFs). Several existing verification techniques attempt to address this problem including SMT-based solvers, mixed-integer programming (MIP), and interval or bound- propagation methods but these approaches often introduce loose, conservative bounds. To overcome these limitations, in this work we use CPED-NCBFs a split-conformal prediction based verification strategy to verify the learned NCBF from the expert demonstrations. We further validate our method on point mass systems and unicycle models to demonstrate the effectiveness of the proposed theory.

Keywords: Autonomous systems, Nonholonomic systems, Control applications
Abstract: Autonomous exploration in complex, unmapped indoor environments presents critical challenges related to safety, coverage, and feasible motion planning. In such settings—particularly in the absence of global positioning systems—exploration strategies become highly deployment-specific, and ensuring safety becomes a significant concern. Traditional grid-based mapping approaches are often sensitive to localization drift and inconsistencies in range measurements, limiting their reliability in real-world scenarios. To address these limitations, this paper proposes a robust exploration strategy that leverages a sparse topological representation to achieve reliable global exploration while ensuring safety. The proposed approach uses integrated planning and control with gap-based graph formation method to avoid trapping in dead-ends and other trap-like configurations—scenarios where purely reactive control is insufficient to guarantee safety. Ex- perimental results demonstrate the effectiveness of the strategy in generating sparse, safety-aware topological maps that enable efficient and reliable navigation, rendering comparable results as conventional resolution-rich grid map-based methods in terms of feasibility and robustness.

Keywords: Adaptive systems, Constrained control, Optimal control
Abstract: This paper presents a biologically inspired motion camouflage technique for target tracking applications. The central idea is that a tracker plans a trajectory such that its motion remains hidden, camouflaged w.r.t. a reference, while approaching a target. Existing work in motion camouflage- based target tracking assumes reference at infinite distance, leading to parallel line of sight conditions. Practically the reference might be located at finite distances w.r.t. the target and the tracker. This article develops a unified framework for motion camouflage based target pursuit that maintains alignment of the tracker with the line-of-sight throughout its motion. A Control Lyapunov Function (CLF) ensures target convergence, and a Camouflage Constraint Function (CCF) enforces the camouflage condition throughout motion. There- fore, a combined CLF-CCF approach is presented for target tracking which works for both infinite and finite reference point camouflage conditions. Simulation results are provided to showcase the proposed approach in diverse target tracking scenarios. This includes synthetic data and aggressive-honey bee target tracking experimental data.

Keywords: Linear systems, Control applications
Abstract: In this paper, we propose a new time scale generation algorithm from an ensemble of hydrogen maser-type atomic clocks by using tools from control theory. We consider another ensemble of cesium-type clocks to optimize the long-term frequency stability of the generated time scale, measured by Hadamard variance. We exploit an observable canonical decomposition of the compact model of the ensembles of cesium- type and hydrogen maser-type clocks to decompose the system into observable and unobservable subsystems. By employing the observable state estimates from Kalman filtering algorithm, we prove that the time deviation of each hydrogen maser-type clock from the ideal time behavior follows the dynamics of the first state variable of a weighted average dynamics associated with cesium-type clocks if certain conditions are satisfied by the state feedback matrices. The synchronized time shared by all the hydrogen maser-type clocks is referred as the generated time scale. We evaluate the Hadamard variance of this weighted average dynamics, and select its weighting vector appropriately to obtain least Hadamard variance of the generated time scale over extended period of time. Numerical example depicts that the resulting time scale has good short-term frequency stability and excellent long-term frequency stability.

Keywords: Robust adaptive control, Aerospace, Nonlinear systems
Abstract: This paper proposes an adaptive predefined-time sliding mode control scheme for spacecraft attitude tracking using four single gimbal control moment gyroscopes (SGCMGs) arranged in a pyramidal configuration. The control framework addresses key challenges such as internal singularities of the SGCMGs, external disturbances, and uncertainties in spacecraft inertia. By integrating an adaptive law within a sliding mode control framework, the controller compensates for unknown disturbances and inertia variations in real-time without requiring prior knowledge of their bounds. A quaternion-based attitude representation is employed for kinematic singularity-free attitude tracking. The stability of the closed-loop system is rigorously analyzed using Lyapunov theory. Simulation results demonstrate the effectiveness of the proposed approach in achieving accurate attitude tracking while successfully avoiding internal singularities of the SGCMG system through the utilization of the null motion.

Keywords: Chaotic systems, Modeling and simulation, Stability of nonlinear systems
Abstract: This paper presents a comprehensive dynamical analysis of a four-dimensional flux-controlled memristor-based hyperchaotic oscillator. The system's complex dynamics are examined using the stability analysis of equilibrium points. Local stability is investigated through linearization about the equilibrium points, providing insight into the system's sensitivity and the nature of its equilibrium points. To characterize the chaotic behavior, a detailed set of numerical diagnostics is employed, including 2D and 3D phase portraits, Poincaré sections, power spectral density, Lyapunov exponent (LE) spectra, and bifurcation diagrams. The simulation results demonstrate complex nonlinear dynamics and more than one positive LE, confirming that the system exhibits hyperchaotic behavior. These properties make the system a promising candidate for application in secure communication, advanced computing architectures, and neuromorphic engineering. Moreover, this study lays the groundwork for future investigations into control strategies and hardware implementations of memristor-based chaotic systems.

Keywords: Neural networks, Control applications, Machine learning
Abstract: On the way to attain energy security and mitigate the climate change related issues, many countries are shifting towards cleaner energy sources. Wind energy, in this regard, has become a promising resource due to its easy availability, cleaner production, and affordability. In studies of wind farm design and control, Wind Frequency Map (WFM) plays a crucial role for its direct involvement in the power calculations. WFM is a joint probability mapping between wind direction and speed considering their time series behavior. Limited amount of wind data, when used for forecasting as well as representing the WFMs, suppresses the ability to represent long term variabilities in wind. This leads to inaccurate estimation of WFM, which in turn results in unrealistic calculation of wind power, leading to nonoptimal design and control. Hence, in this paper, Temporal Convolutional Neural Networks (TCNNs) are explored for accurate forecasting of WFMs. As determination of hyper-parameters are performed heuristically as a general practice, the novelty in the proposed formulation lies in determining these parameters through an optimization formulation while performing the network training simultaneously. Effectiveness of this approach, while developing realistic wind forecasting models, has been demonstrated further by showing its ability in accurate power calculations.

Keywords: Mechanical systems/robotics, Flight control, Control applications
Abstract: Nowadays, Unmanned Aerial Vehicles (UAVs) are extensively applied in diverse fields, including surveillance, delivery, agriculture, mapping, and aerial photography. This study focuses on employing a data-driven control strategy for trajectory tracking in a quadrotor-type UAV, encompassing various trajectories such as circular and Lissajous types. The mathematical model of a quadrotor with an X-configuration is established using the Newton-Euler method. Simulations are conducted in MATLAB/Simulink to elucidate the quadrotor dynamics with data-driven control. The simulation results (obtained through analytical modelling) were refined through data-driven modelling, treating the quadcopter system as a black-box model to enhance trajectory-tracking accuracy. The application of data-driven modelling enhances trajectory tracking accuracy, showcasing the effectiveness of this technique.

Keywords: Automotive, Control applications
Abstract: Intelligent Transportation Systems (ITS) have re- defined modern mobility by employing real-time communica- tion and coordination. But, reliance of safety-critical services like vehicle platooning on communication and computing re- sources has exposed them to potential cyber-attacks. This paper utilises a deterministic model of cyber-threats to arrive at the bounds of a class cyber-attacks to which the sliding mode control (SMC)-based heavy commercial road vehicle (HCRV) platoons are resilient. As real-time assessment of cyber-attacks is not feasible, a HCRV platoon model, encompassing inherent system non-linearities and actuator dynamics, is utilised for the impact analysis. Different safety and performance metrics of the platoon are evaluated for nominal conditions and across different classes of attacks. The FDI attack scenario evaluated was characterised by a significant crash metric score of 35.35% which in turn indicates a significant deviation from the safe time headway to be maintained for collision-free operation. The platoon operation under the modelled delay attack was found to be collision-free with a 0 crash metric score. However, efficiency and passenger comfort levels are significantly compromised, as denoted by a waste metric score of 2.27 s and discomfort index of 3.71 m/s3. The study highlights the safety threats posed by realistic cyber-attacks on vehicular platoons, emphasizing the need for incorporating resilient platoon controller design.

Keywords: Robust control, Predictive control for linear systems, Autonomous systems
Abstract: This paper presents a novel control framework that integrates Model Predictive Control (MPC), Discrete Integral Sliding Mode (DISM), and Control Barrier Functions (CBFs) to ensure safe, robust, and optimal navigation in uncertain environments. While MPC enables optimal trajectory planning under constraints, CBFs ensure forward invariance for proactive obstacle avoidance and DISM offers robustness against matched disturbances and model uncertainties from the initial time step. The unified framework addresses limitations in existing MPC-CBF and MPC-DISM combinations, particularly in handling disturbances and ensuring real-time safety. Simulation results show that, unlike standard MPC-CBF, the proposed controller maintains feasibility and performance even under external disturbances.

Keywords: Autonomous systems, Aerospace, Nonholonomic systems
Abstract: This study focuses on reducing the convergence time for path-following problems of Unmanned Aerial Vehicles (UAVs) operating at a constant altitude by adopting a non-linear guidance law based on Fast Terminal Sliding Mode Control (FTSMC). The control law is designed with consideration for the Lyapunov stability criterion and reachability conditions aimed at nullifying the perpendicular distance between the UAV and the desired path. The rate of convergence increases in the vicinity of the equilibrium state so that the overall convergence time is reduced. The effectiveness of the proposed algorithm is validated through numerical simulations and comparisons with some existing sliding mode path-following strategies.

Keywords: Networked control systems, Robust control, Observers for linear systems
Abstract: This paper presents an observer-based dynamic event-triggered sliding mode control for a chain of integrator systems in the presence of matched uncertainties. To estimate the unmeasured states, a higher-order sliding mode observer is employed, which ensures that the observed states converge to the actual states in finite time. A dynamic event condition is designed using the observed state to increase the time interval between two consecutive control updates. This condition depends on a threshold which evolves with time and results in fewer control updates than a static event-triggering condition, without compromising the system performance. The key idea is to show the stability of the system with the proposed dynamic event-triggered control using the observed states obtained from observers of two different orders. Additionally, a lower bound on the time elapsed between two consecutive triggering events is established to guarantee the avoidance of Zeno behavior. A simulation for a numerical example of a 3rd-order integrator is provided to validate theoretical findings and demonstrate the effectiveness of the proposed method.

Keywords: Control applications, Stability of nonlinear systems, Robust control
Abstract: This paper presents a disturbance analysis for a quadcopter system using various disturbance models. A super-twisting sliding mode controller (STSMC) is developed to address the disturbances modeled by the Von Karman wind turbulence model (VKWTM), Dryden wind turbulence model (DWTM), and a time-varying disturbance (TVD) profile. These disturbance models are employed to simulate turbulent wind gusts for disturbance modeling. The stability of the proposed control scheme is rigorously verified using Lyapunov stability theory. The performance of VKWTM, DWTM, and TVD, in terms of tracking error and control effort, is evaluated through a comparative table. A series of simulations are conducted in MATLAB, incorporating each disturbance model to evaluate the controller’s performance. The simulation results demonstrate the effectiveness and robustness of the proposed control strategy under different disturbance conditions.

Keywords: Robust adaptive control, Neural networks, Nonlinear systems
Abstract: This paper presents a new neuro-adaptive control framework that combines backstepping and integral sliding mode control for strict-feedback nonlinear systems with unknown dynamics and external disturbances. The proposed Neuro-adaptive backstepping integral sliding mode control (NA-BISMC) technique integrates radial basis function neural networks to estimate the unknown nonlinearities in online mode, and the integral sliding surface ensures robustness. Furthermore, a performance function is introduced to ensure that the tracking error evolves strictly within predefined limits, achieving a user-defined convergence rate, maximum overshoot, and steady-state accuracy. In comparison to the previous literature, the proposed controller offers improved robustness, superior transient performance and prescribed performance bound for all the error states. The Lyapunov-based stability proof confirms the finite-time stability of the integral sliding surface and the prescribed performance bound stability of all error states in the closed-loop system. The simulation results validate the effectiveness of the proposed controller and demonstrate its improved performance in terms of tracking error when compared with the available literature.

Keywords: Mechanical systems/robotics, Biomedical, Adaptive systems
Abstract: Lower-limb exoskeletons offer effective active-assist solutions for rehabilitating individuals with gait and mobility impairments. This study proposed a human-in-the-loop control framework for a 3-DOF unilateral exoskeleton for the active assistance of a pediatric gait. The proposed HIL strategy incorporates the variable admittance control in the outer loop and non-singular terminal sliding mode (NSTSM) in the inner loop to enhance the trajectory tracking and offer safety and flexibility to the user. The variable admittance control modulates the admittance parameters of the exoskeleton using a simple adaptive law based on the human-exoskeleton interaction, while the NSTSM control provides a robust and accurate gait tracking at different walking speeds. Numerical simulations demonstrate that the HIL framework with variable admittance control effectively improves the safety and flexibility for pediatric users under human-exoskeleton interaction compared to the HIL framework with fixed admittance control. The fixed admittance parameters of stiffness (100 Nm/rad) and damping (2 Nms/rad) appear misestimated, as compliance modulation suggests varying parameters (65–95 Nm/rad and 1.8–2.7 Nms/rad) for improved adaptability to interactions.

Keywords: Modeling and simulation, Stability of linear systems, PID control
Abstract: This paper presents a nonlinear mathematical model of the once-through type Helical Coil Steam Generator (HCSG). This model is developed using the fundamental principles of mass and energy conservation, to support control system design by capturing the key dynamics of the Steam Generator (SG) accurately. The model is validated through open loop simulations and results are benchmarked against reference model. To facilitate controller design, the nonlinear model is linearized around an equilibrium point to obtain a corresponding linearized model. The properties of the linearized model are studied, and the transient response of linear model is compared with that of the original nonlinear model. Based on the linearized model a Proportional Integral (PI) controller is designed. Finally, the controller’s performance is evaluated through stability analysis and simulations conducted on the original nonlinear model.

Keywords: Identification, Emerging control applications, Grey-box modeling
Abstract: The paper presents a low-order dynamic model of a thermal zone that is served by an inverter air conditioner (AC), and a method to identify the model from measurements. The motivation for the model comes from its application in demand dispatch, in which many ACs are coordinated to manipulate aggregate demand while ensuring each AC maintains its indoor temperature within bounds. The presence of unmeasured disturbance from heat gains due to occupants and appliances is a challenge. The power consumption model of an inverter AC can be overly complex or unrealistically simple; striking the right balance is challenging. We use a first-order RC network model for the thermal dynamics sub-model and cast the identification problem as a convex optimization problem. An ℓ₁ penalty enables estimating the unmeasured heat gain assuming that it is piecewise constant. The power consumption is modeled as a static nonlinearity with a first-order filter in series. Experimental data from a testbed in IIT Guwahati is used to fit the sub-models. The data also shows that for the specific AC used, there are additional features in the power consumption compared to what is hypothesized in recent related work.

Keywords: Estimation, Hybrid systems, Discrete event systems
Abstract: Applications of Multilevel converters (MCs) can be found in the fields of power systems, electric vehicles, industrial drives, aerospace, etc. The operation of the MCs requires the measurement of voltages across the capacitor, as this is required for circulating current suppression, voltage balancing and its control. However, putting the physical sensors for this makes the complete MC design bulky, complex, and costly. Thereby, an estimation strategy can replace the need for multiple sensors and make the design simple, compact and cheap. However, the estimation strategy varies for different MC topologies, which leads to additional analysis, leading to difficulty in implementation. Hence, to address this problem, first, a novel conditional switched system (CSS) is proposed, which exhibits dynamic behaviour similar to that of various MC topologies. Then, its state estimation problem is addressed. The state estimation of the system CSS under consideration is challenging as it is unobservable when analyzed as either a linear or a nonlinear system. However, it becomes observable when treated as a hybrid system, despite potentially having unstable invariant zeros. To address the state estimation challenge, a discrete-time sliding mode observer (DTSMO) is introduced for this hybrid system. A simulation study is undertaken considering a modular multilevel converter (MMC) as a case of CSS, and an estimation of sub-module capacitor voltages is demonstrated.

Keywords: Robust adaptive control, Direct adaptive control, Power systems
Abstract: This paper proposes an the algorithm for designing Adaptive Sliding Mode Control (ASMC) with Gao’s reaching law.Robustness of the proposed algorithm is evaluated for a two-area interconnected power system model addressing load frequency control problem. The gains of the controller are adaptively tuned to ensure lower control input and mitigate chattering. Specifically, the gains of Gao’s reaching law are adapted as a nonlinear function of the sliding surface, which is the foundation for the proposed ASMC law. Satisfactory performance of the proposed ASMC approach is verified from the perspective of stability. The asymptotic stability of the control law is theoretically established using a Lyapunov-based stabilityanalysis. The proposed ASMC law is validated in simulation,and relative performance comparison with conventional SMC and PID controller demonstrates the efficacy of the ASMC approach.

Keywords: Control applications, Optimization, Neural networks
Abstract: Optimal control of hybrid vehicles involving a battery and a combustion engine is necessary for efficient energy management, and this optimal control problem (OCP) can be formulated as a mixed-integer program (MIP). The real-time deployment of MIPs is challenging due to their computational complexity. As the number of integer variables increases, the solution space increases exponentially, requiring computationally expensive methods to solve. Although machine learning-based strategies help reduce computation time, they provide approximate, but not exact, solutions to MIPs and may even involve expensive one-time training, employing heavy computational resources. In this work, a multiparametric (mp) programming framework is used to improve the computation time of an OCP for hybrid vehicles while getting the exact solution; furthermore, the mp-programming framework allows for implementation through low-cost hardware like a chip without needing a computer. In the mp-programming approach, the solution is expressed as a set of piecewise linear functions in terms of the uncertain parameters that change with time. During online calculations, the optimal solution is determined through a point location search procedure. This framework is compared with the state-of-the-art branch-and-bound method in the Gurobi solver and a neural network solver model developed in this work.

Keywords: Autonomous systems, Decentralized control, Cooperative control
Abstract: In this letter, we address synchronization in networked dynamical descriptor systems (NDDS), where followers must synchronize with the leader's state. We propose a novel distributed proportional-derivative (PD) feedback control design procedure to achieve this. Specifically, local distributed derivative feedback is used to increase the dynamical order of the global synchronization error dynamics, which initially lack sufficient order due to algebraic constraints. Additionally, local distributed proportional control ensures synchronization of the followers with the leader's dynamics. Simulations are also presented to vindicate the theoretical claims.

Keywords: Nonholonomic systems, Constrained control, Nonlinear systems
Abstract: This paper presents a predefined-time circumnavigation strategy for an unmanned aerial vehicle (UAV) modeled by nonholonomic kinematics, tasked with tracking a stationary and a moving target under wind disturbances. Unlike existing methods that offer asymptotic or finite-time convergence without explicit control over convergence time, our framework guarantees that the UAV reaches a desired circular path around the target within a user-specified time bound. We design a Lyapunov-based guidance vector field in the target-relative frame to drive the radial tracking error to zero in predefined-time. To maintain a constant commanded airspeed while compensating for wind and target motion, we derive a scaling factor that adjusts the guidance vector field while preserving its Lyapunov-based convergence properties. A nonlinear program is solved at each timestep to ensure the UAV’s heading converges to the desired heading angle within the predefined-time while respecting actuator limits. Numerous simulation results depicting various cases are presented justifying the efficacy of the proposed methodology.

Keywords: Nonlinear systems, LMIs, Feedback linearization
Abstract: This paper presents a novel methodology to reduce computational burden of convex control techniques in single-input single-output systems, which rapidly grows as the number of nonlinearities increase. The proposal is based on partial feedback linearization to transform the nonlinear system into another one via a properly chosen local diffeomorphism. The transformed system has fewer nonlinearities than the original and a stabilizing control law can be then calculated as a feedforward term cancelling some nonlinearities plus a feedback nonlinear one mimicking parallel distributed compensation. Simulation examples put our proposal at test and provide comparisons on numerical complexity which help the reader to assess the contribution.

Keywords: Distributed parameter systems, Numerical algorithms
Abstract: We consider an 1D partial integro-differential equation (PIDE) comprising of an 1D parabolic partial differential equation (PDE) and a nonlocal integral term. The control input is applied on one of the boundaries of the PIDE. Partitioning the spatial interval into n+1 subintervals and approximating the spatial derivatives and the integral term with their finite-difference approximations and Riemann sum, respectively, we derive an n^{rm th}-order semi-discrete approximation of the PIDE. The n^{rm th}-order semi-discrete approximation of the PIDE is an n^{rm th}-order ordinary differential equation (ODE) in time. We establish some of its salient properties and using them prove that the solution of the semi-discrete approximation converges to the solution of the PIDE as ntoinfty. We illustrate our convergence results using numerical examples. The results in this work are useful for establishing the null controllability of the PIDE considered.

Keywords: Variational methods, Optimal control, Numerical algorithms
Abstract: This paper presents a rigorous unification of minimal energy control theory and variational methods in analytic number theory, drawing inspiration from Srinivasa Ramanujan’s seminal work. We develop detailed formulations with complete derivations for (1) optimal control of prime distributions, (2) variational extremization of arithmetic functions, and (3) theta function-based lattice energy minimization. Each problem is treated with full mathematical rigor while maintaining accessibility through step-by-step explanations of all calculations and motivations. The paper establishes novel connections between these disparate areas while providing concrete, implementable results.

Keywords: Stochastic systems, Control applications, Nonlinear systems
Abstract: This article discusses the solvability and trajectory controllability for the second-order hyperbolic Gurtin-Pipkin type stochastic integro-differential equations driven by the Rosenblatt process within the Hilbert space. To capture the random effects, the deterministic Gurtin-Pipkin equation is incorporated with stochastic perturbations. The existence and uniqueness of a mild solution for the proposed system were discussed using the semigroup theory, stochastic analysis, and fixed point technique. In addition, the trajectory controllability of the considered system is presented by defining a suitable feedback controller and using the Gronwall's inequality.

Keywords: Machine learning
Abstract: In this talk, we discuss discrete diffusion models that offer a unified framework for jointly modeling categorical data such as text and images. We first discuss a new model we have developed for language generation called the Anchored Diffusion Language Model (ADLM). ADLM is grounded in a novel two-stage framework that first predicts distributions over important tokens via an anchor network (e.g., key words or low-frequency words that anchor a sentence), and then predicts the likelihoods of missing tokens conditioned on the anchored predictions. ADLM significantly improves test perplexity on LM1B and OpenWebText, achieving up to 25.4% gains over prior DLMs, and narrows the gap with strong AR baselines. It also achieves state-of-the-art performance in zero-shot generalization across seven benchmarks and surpasses AR models in MAUVE score, which marks the first time a DLM generates better human-like text than an AR model. We next discuss posterior sampling for images using pretrained discrete diffusion foundation models, aiming to recover images from noisy measurements without retraining task-specific models. We introduce Anchored Posterior Sampling (APS) for masked diffusion foundation models, built on two key innovations—quantized expectation for gradient-like guidance in discrete embedding space, and anchored remasking for adaptive decoding. Our approach achieves state-of-the-art performance, and demonstrates training-free stylization and text-guided editing using our ...

Keywords: Control applications
Abstract: The electrification of mobility is fundamentally reshaping energy demand across urban infrastructure and enterprise operations. Electric vehicles (EVs) are evolving beyond their traditional role as transport assets to become mobile energy buffers that are capable of dynamic interaction with buildings and the grid. This talk will present a unified approach to leveraging EVs for cost-effective energy management in two complementary domains: smart buildings and logistics operations. In the first part, we introduce a multi-agent reinforcement learning framework for the joint control of HVAC systems and EVs within buildings. By modeling EVs as stochastic buffers, the framework accounts for uncertain availability while maintaining thermal comfort and respecting state-of-charge constraints. This enables adaptive load shaping under time-of-day pricing regimes, leading to significant energy cost savings. The second part focuses on enterprise-level energy optimization, where EV fleet routing is tightly coupled with energy procurement decisions across multiple sources. We propose a hybrid iterative optimization framework that combines Genetic Algorithms for energy sourcing with the Coronavirus Herd Immunity Optimizer for fleet routing, ensuring scalability across diverse service locations and fleet sizes. Together, these frameworks demonstrate how EVs can be orchestrated as intelligent agents within cyber-physical systems for cost-effective operations.

Keywords: Iterative learning control

Keywords: Mechanical systems/robotics
Abstract: Motion planning, the task of finding a collision-free path in the presence of obstacles, lies at the heart of robotics. Equally important, yet often less explored, is the question of infeasibility, which concerns determining path non-existence. In task and motion planning, the ability to assess motion (in)feasibility is essential for robust decision-making and reliable execution of high-level tasks. When infeasibility is detected, alternative strategies or task plans must be formulated. Feasibility assessment also plays a critical role in manipulation amidst clutter and in rearrangement planning. In the Navigation Among Movable Obstacles (NAMO) paradigm, when no feasible path exists, obstacles are repositioned to create navigable paths.

This talk will explore why infeasibility detection is inherently challenging, present approaches for identifying and certifying infeasibility, and discuss methods for reasoning about its underlying causes within motion planning frameworks.

Keywords: Estimation
Abstract: This talk is concerned with the problem of state estimation for discrete-time linear systems in the presence of additional (equality or inequality) constraints on the state (or estimate). By use of the minimum variance duality, the estimation problem is converted into an optimal control problem. Two algorithmic solutions are described: the full information estimator (FIE) and the moving horizon estimator (MHE). The main result is to show that the proposed estimator is stable in the sense of an observer.

Keywords: Agents-based systems
Abstract: Demand response involves system operators using incentives to modulate electricity consumption during peak hours or when faced with an incidental supply shortage. However, system operators typically have imperfect information about their customers’ baselines, that is, their consumption had the incentive been absent. The standard approach to estimate the reduction in a customer’s electricity consumption then is to estimate their counterfactual baseline. However, this approach is not robust to estimation errors or strategic exploitation by the customers and can potentially lead to overpayments to customers who do not reduce their consumption and underpayments to those who do. Moreover, optimal power consumption reductions of the customers depend on the costs that they incur for curtailing consumption, which in general are private knowledge of the customers, and which they could strategically misreport in an effort to improve their own respective utilities even if it deteriorates the overall system cost. The two-stage mechanism proposed in this paper circumvents the aforementioned issues. In the day-ahead market, the participating loads are required to submit only a probabilistic description of their next-day consumption and costs to the system operator for day-ahead planning. It is only in real-time, if and when called upon for demand response, that the loads are required to report their baselines and costs. They receive credits for reductions below their reported baselines ...

Keywords: Networked control systems
Abstract: In networked control systems, consensus based algorithms are widely used for distributed optimization, sensor fusion, formation control etc. In this talk, we consider a finite-duration, magnitude bounded false data injection (FDI) attack on consensus network. The goal of the attacker is to induce maximum disagreement between nodes and consequently, influence the convergence of the consensus algorithm. We obtain closed-form expressions for the optimal attack input which results in the maximum disagreement and the corresponding value of disagreement. Further, the effect of varying attack duration on the induced disagreement is analyzed. Finally, it is shown that the criticality of nodes, measured as the disagreement induced by attack on them, has strong negative correlation with their degrees.