Keywords: Mechatronic systems
Abstract: Magnetic hysteresis in the core of actuators at various air gaps limits the use of high-precision applications and motion systems. The Gap-dependent Prandtl-Ishlinskii hysteresis model is proposed to reduce magnetic hysteresis at different air gaps. The inverse of the model is then used as a feedforward compensation with the control law to compensate for hysteresis. The electromagnetic actuator model incorporates the hysteresis effect and a variable air gap. The Gap-dependent Prandtl-Ishlinskii model is developed, combining the deadzone model with the Prandtl-Ishlinskii model to describe the saturation and gap dependency of magnetic hysteresis. The proposed model is employed in formulating the inverse model for feedforward hysteresis compensation. Finally, the flux tracking error bound for the feedforward controller with and without hysteresis compensation is calculated and compared. A numerical demonstration example is proposed to demonstrate the effectiveness of the proposed model. The simulation results demonstrate that the proposed model can account for hysteresis nonlinearities at different air gaps. At an air gap of 0.075mm, using the inverse model as a feedforward control law for hysteresis compensation reduces tracking to 3.5 % compared to 14.5 % without compensation.

Keywords: Nonlinear control, Unmanned systems, Robust control
Abstract: This paper presents a backstepping control strategies considering constraints for quadrotor unmanned aerial vehicle system. Based on the barrier Lyapunov theory, the backstepping control approach is designed to not only follow the desired trajectory but also to strictly enforce altitude, position and Euler angles constraints. The controller incorporates both asymmetric and symmetric barrier Lyapunov functions. Moreover, it is demonstrated that the proposed controllers guarantee the stability of the unmanned aerial vehicle system, ensuring asymptotic convergence. To validate the effectiveness of the proposed techniques, these latter are tested in numerical simulations using the Parrot Mambo drone model where the objective is to control the altitude, the position and the attitude.

Keywords: Nonlinear systems, Optimisation, Sampled-data systems
Abstract: The paper is dedicated to data-driven analysis of dynamical systems. It deals with certifying the basin of attraction of a stable equilibrium for an unknown dynamical system. It is supposed that point-wise evaluation of the right-hand side of the ordinary differential equation governing the system is available for a set of points in the state space. Technically, a Piecewise Affine Lyapunov function will be constructed iteratively using an optimisation-based technique for the effective validation of the certificates. As a main contribution, whenever those certificates are violated locally, a refinement of the domain and the associated tessellation is produced, thus leading to an improvement in the description of the domain of attraction.

Keywords: Nonlinear systems, Optimisation, System identification
Abstract: The precise identification of Brushless DC (BLDC) motor parameters is important for accurate modeling, control, and sensorless control in various applications. This paper presents an identification approach based on optimization algorithms to estimate key motor parameters, including stator resistance, inductance, torque constant, moment of inertia, friction coefficient. The methodology involves performing tests while acquiring real-time voltage and current measurements and the rotor speed. A metaheuristic optimization algorithm, namely the Salp Swarm Algorithm (SSA), is employed to minimize an objective function that quantifies the error between the simulated and measured motor responses. The Simulink model of the BLDC motor is used to evaluate different parameter sets iteratively. The results demonstrate the effectiveness of the proposed approach in accurately estimating motor parameters.

Keywords: Robotics, Navigation, Autonomous systems
Abstract: This paper presents a novel method to navigate a sensorless autonomous mobile robot in indoor environments. Fixed sensor networks and computing hardware are used to monitor targets and the robot continuously. The fixed infrastructure component localises the mobile robot, simultaneously tracks all dynamic obstacles and generates the robot’s trajectory. The authors have implemented the Interacting Multiple Model Joint Probabilistic Data Association (IMM-JPDA) filter to track multiple targets, including the robot, in cluttered environments. The proposed innovative approach separates the tasks of sensing, trajectory planning and low-level control, where mobile robots only perform low-level control. High-fidelity simulations are performed in MATLAB software to illustrate how the presented solution works.

Keywords: Robotics, Robust control, Nonlinear control
Abstract: This work proposes a robust sliding mode controller for robotic systems, incorporating prescribed performance theory to address the chattering problem and enhance tracking accuracy during both reaching and sliding phases. Firstly, the tracking error will be transformed and a modified sliding surface that will be forced to evolve inside prescribed constraints will be proposed. Secondly, the designed surface will be also transformed to impose other constraints to keep the trajectories in a small region around the manifold. Finally, an adaptive fixed-time reaching law is proposed for fast response during the reaching phase. The controller will be designed while considering the problem of uncertainties. The suggested method is evaluated through simulation on a 2-link robotic arm and contrasted with the traditional sliding mode to highlight the enhancements.

Keywords: Robotics, Robust control
Abstract: In this paper, we propose a novel learning-based robust feedback linearization strategy to ensure precise trajectory tracking for an important family of Lagrangian systems. We assume a nominal knowledge of the dynamics is given but no a-priori bounds on the model mismatch are available. In our approach, the key ingredient is the adoption of a regression framework based on Gaussian Processes (GPR) to estimate the model mismatch. This estimate is added to the outer loop of a classical feedback linearization scheme based on the nominal knowledge available. Then, to compensate for the residual uncertainty, we robustify the controller including an additional term whose size is designed based on the variance provided by the GPR framework. We proved that, with high probability, the proposed scheme is able to guarantee asymptotic tracking of a desired trajectory. We tested numerically our strategy on a 2 degrees of freedom planar robot.

Keywords: Swarms, Robotics, Formation control
Abstract: This paper presents a UAV swarm system designed to assist first responders in disaster scenarios like wildfires. By distributing sensors across multiple agents, the system extends flight duration and enhances data availability, reducing the risk of mission failure due to collisions. To further mitigate this risk, we introduce an autonomous navigation framework that utilizes a local Euclidean Signed Distance Field (ESDF) map for obstacle avoidance while maintaining swarm formation with minimal path deviation. Additionally, we incorporate a Traveling Salesman Problem (TSP) variant to optimize area coverage, prioritizing Points of Interest (POIs) based on preassigned values derived from environmental behavior and critical infrastructure. The proposed system is validated through simulations with varying swarm sizes, demonstrating its ability to maximize coverage while ensuring collision avoidance between UAVs and obstacles.

Keywords: Unmanned systems, Robotics, Autonomous systems
Abstract: Multi-UAV Coverage Path Planning (mCPP) algorithms in popular commercial software typically treat a Region of Interest (RoI) only as a 2D plane, ignoring important 3D structure characteristics. This leads to incomplete 3D reconstructions, especially around occluded or vertical surfaces. In this paper, we propose a modular algorithm that can extend commercial two-dimensional path planners to facilitate terrain-aware planning by adjusting altitude and camera orientations. To demonstrate it, we extend the well-known DARP (Divide Areas for Optimal Multi-Robot Coverage Path Planning) algorithm and produce DARP-3D. We present simulation results in multiple 3D environments and a real-world flight test using DJI hardware. Compared to baseline, our approach consistently captures improved 3D reconstructions, particularly in areas with significant vertical features. An open-source implementation of the algorithm is available here: https://github.com/konskara/TerraPlan

Keywords: Autonomous systems, Image processing, Unmanned systems
Abstract: This paper presents a new and comprehensive autonomous framework for wildfire detection, global localization, and suppression leveraging Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs). This work significantly expands upon existing research focusing solely on UAV-based wildfire mitigation. By synergistically combining the capabilities of UAVs with those of UGVs, our system enhances situational awareness and operational effectiveness. Notably, this approach integrates cutting-edge localization technologies, including the Simultaneous Localization and Mapping (SLAM) system for accurate global mapping of fire spots to enable near-meter level precision tracking of wildfires even when obscured from view by vegetation or debris. To further enhance accuracy, we employ a motion model and an Iterated Extended Kalman Filter (iEKF) incorporating both Global Navigation Satellite Systems (GNSS) data and semantic vision information. Extensive simulations in cluttered environments confirm the efficacy of our proposed framework in supporting timely and accurate wildfire detection and suppression.

Keywords: Neural networks, Optimisation, Power systems and smart grid
Abstract: This paper presents an original application of physics-informed neural networks (PINN) to the problem of computing the critical clearing time in power system rotor-angle transient stability studies. We first analyze the accuracy of a properly trained PINN in terms of its ability to correctly reproduce the dynamics of a generator - infinite bus system evolving from initial conditions next to the boundary of the region of attraction of its unique stable equilibrium. We then integrate the PINN in a mixed-integer linear programming problem that, assuming no prior knowledge about the region of attraction, provides the critical clearing time of the system and, as a by-product, the system trajectories characterizing the full fault and clearing process. The simulation results are presented to validate the proposed approach.

Keywords: Fault diagnosis
Abstract: This article investigates detecting faults in EV (electric vehicle) charging stations using only the sensor measurements. The sensor measurements are related through mathematical operators called transmissibilities. EV charging stations are high-power electrical equipment placed in crowded areas that directly interact with users. These stations connect the EV to an electricity source for fast recharging of the EV battery. A fault in these charging stations, such as short or open circuits, might lead to catastrophic losses. The unexpected operating conditions are a challenge, as users might join at a time. On the other hand, transmissibility operators are independent of any external excitations on the station, such as station inputs, external disturbances, and the user's operation. This work exploits the transmissibility advantage in handling random operating conditions and estimating the output voltage profile. This estimation is then used for fault detection and health monitoring. The potential of the proposed approach is illustrated through a simulation example.

Keywords: Robust control, Nonlinear control, Renewable energy and sustainability
Abstract: This work implements a new control strategy for the collective blade pitch (CBP) control of floating wind turbines (FWTs) operating above the rated wind speed (Region III). The primary objectives are to regulate power output, limit platform pitch motion, and reduce fatigue loads on the blades. To address the challenges posed by the uncertain and nonlinear dynamics of FWTs, the proposed scheme incorporates adaptive super-twisting (ASTW) control with self-tuning capabilities. This approach utilizes adaptive time-varying gains to accommodate unknown perturbation bounds, ensuring robust performance with reduced tuning effort. Key components of the control strategy include the definition of time-varying parameters that adapt and tune themselves based on available information and prevailing conditions. Care is taken to prevent excessively large adaptive gains. The efficacy of this proposed controller is evaluated through a simulation framework that combines Matlab/Simulink with the OpenFAST simulator. The study utilizes the nonlinear OC3-Hywind 5MW FWT model under varying wave and wind conditions. The results demonstrate effectiveness in enhancing FWT performance and its potential to contribute to the sustainable utilization of offshore wind energy resources

Keywords: Robust control, Optimisation, Nonlinear control
Abstract: Generalized semi-infinite programs (generalized SIPs) are problems featuring a finite number of decision variables but an infinite number of constraints. They differ from standard SIPs in that their constraint set itself depends on the choice of the decision variable. Generalized SIPs can be used to model robust optimal control problems where the uncertainty itself is a function of the state or control input, allowing for a less conservative alternative to assuming a uniform uncertainty set over the entire decision space. In this work, we demonstrate how any generalized SIP can be converted to an existence-constrained SIP through a reformulation of the constraints and solved using a local reduction approach, which approximates the infinite constraint set by a finite number of scenarios. This transformation is then exploited to solve nonlinear robust optimal control problems with state-dependent uncertainties. We showcase our proposed approach on a planar quadrotor simulation where it recovers the true generalized SIP solution and outperforms a SIP-based approach with uniform uncertainty bounds.

Keywords: Robust control, Predictive control, Mechatronic systems
Abstract: This paper proposes a speed control approach for a Permanent Magnet Synchronous Motor (PMSM) based on the finite control set model predictive control (FCS-MPC) strategy. It utilizes a cascaded control scheme comprising an inner FCS-MPC current control loop and an outer Sliding Mode Control (SMC) loop. PMSMs are extensively used in industry, and FCS-MPC has been developed for current control due to its fast dynamic response, capability to enforce constraints, and potential elimination of a modulator. Additionally, SMC has gained recognition as an effective approach for controlling PMSMs, thanks to its robustness against model uncertainties and disturbances. The proposed approach, when compared with the conventional PI cascaded control usually implemented by Whirlpool on domestic machines, has demonstrated a noticeable improvement in terms of control performances and robustness.

Keywords: Robust control, Robotics, Nonlinear control
Abstract: This paper investigates a finite-time control strategy for robotic manipulators based on sliding mode control principles. Given the nonlinear and coupled dynamics of robotic manipulators, achieving trajectory tracking under disturbance or unknown dynamics remains a challenge. The proposed control scheme ensures finite-time convergence through a nonlinear sliding surface. Theoretical validation is conducted using Lyapunov-based stability analysis. Simulation results demonstrate the controller’s effectiveness in achieving accurate trajectory tracking.