Keywords: Robotics, Biologically inspired systems, Image processing
Abstract: Robotic tactile sensing can be grouped into intrinsic, placed within the mechanical structure of the robot, estimating contact locations and forces from force sensors, and extrinsic, mounted at the contact interface, and dealing with intrinsic tactile data. In the former category, it is worth mentioning Intrinsic Tactile Sensing (ITS). ITS is a technique that relies on force/torque measurements, and on a priori knowledge of the geometry of the exploring surface, to approximate the distribution of compressive tractions on the surface with a resultant contact force and moment applied to the contact centroid (contact sensing problem). These quantities are fundamental for grasping planning, control, and assessment. ITS is a well-established technique with rigid surfaces, but it is ill-suited for soft deformable materials, although some solutions to deal with this case have been proposed. For the extrinsic sensors, soft optical tactile sensors, which exploit vision information to infer contact properties, have emerged as a promising solution to estimate object tactile properties, but they have never been used to address the contact sensing problem. Integrating the characteristics of ITS with soft optical tactile sensing could significantly advance the perceptual and grasping properties of robotic hands endowed with soft fingers. This work aims to bridge this gap, proposing the integration of ITS with the TacTip, a marker-based soft optical sensor. Experiments that validate the proposed approach are discussed.

Keywords: Robotics, Cyber-physical systems, Intelligent control systems
Abstract: This paper introduces Exploration-Boosted MC-PILCO (EB-MC-PILCO), a novel framework that synergizes MC-PILCO, a state-of-the-art probabilistic model-based reinforcement learning algorithm, with iLQR, a trajectory optimization algorithm. While MC-PILCO excels in sample efficiency, its computational demands can entail high convergence time. EB-MC-PILCO addresses this by first leveraging iLQR to rapidly generate exploratory trajectories and initialize MC-PILCO’s policy, thus reducing policy optimization steps and convergence time. Extensive simulations on a cart-pole environment confirm that this strategy can substantially reduce MC-PILCO convergence time

Keywords: Robotics, Mechatronic systems, Unmanned systems
Abstract: Aerial vehicles are increasingly utilized across a wide range of application domains in which their primary functions often involve monitoring, inspection, and vision-based operations. This paper explores a novel and under-explored application area for aerial vehicles: the autonomous selection and deployment of modular and reconfigurable structures. Specifically, in the broader context of Modular Self-Reconfigurable Robots (MSRR), the study examines the state-of-the-art control techniques that enable aerial vehicles to autonomously select proper structures depending on the considered working environment and self-reconfigure in the selected one. These structures hold significant potential for addressing challenges in emergency and critical scenarios, such as storms, floods, earthquakes, and fires. In such situations, a fleet of aerial vehicles can be requested to rapidly reach critical areas to transport useful materials and then self-assembly into reconfigurable structures of practical use, such as footbridges or complex structure inside spaces with narrow entrance, or in general structures to help endangered people or support first responders in their search and rescue activities. The surveyed articles are classified by considering two major control problems, i.e., reconfiguration planning and control, and task-shape matching. A discussion is provided regarding, on the one hand, the well established control methods for MSRR and their adaptability to aerial MSRR (AMSRR), and on the other hand the advancements and challenges of current control algorithms for AMSRR, identifying open issues and future research paths.

Keywords: Robotics, Multi-agent systems, Optimisation
Abstract: This paper presents a novel optimization-based approach for the synthesis of haptic feedback within human-multi-robot systems in the context of multi-task execution scenarios. The proposed control framework consists of an optimization algorithm designed to allocate tasks to individual robots and to synthesize a haptic feedback control signal for a human operator that interfaces with the team of robots via a haptic device. The interaction controller is implemented as a convex optimization policy, which is amenable for online implementation with real-time constraints, even in presence of a high number of tasks and robots. The behavior of the proposed approach is illustrated via experiments, where a team of ground mobile robots execute multiple tasks and interacts with a human operator.

Keywords: Robotics, Multi-agent systems, Optimisation
Abstract: This paper presents an optimization-based control framework for energy-aware control of a heterogeneous robotic system deployed for long-term environmental monitoring applications. The robotic system is comprised of a mobile charging station controlled to provide the required energy resources to a robot whose tasks have to be executed continuously and persistently. The charging station is assumed to be able to harvest energy from the environment (e.g. via solar panels) and is controlled to maximize the collected energy. We propose suitable models for the energy dynamics of both robots, as well as models for the environment dynamics. The energy sufficiency of the heterogeneous robotic system is translated into the forward invariance of a subset of the system state space and turned into a control input constraint by leveraging Control Barrier Functions (CBFs). The resulting control algorithm is a convex optimization problem that is solved online. The feasibility of the optimization problem is analyzed and sufficient conditions to ensure it are provided in terms of model parameters. The effectiveness of the controlled system at ensuring energy awareness is validated both in simulation as well as on real robotic platforms.

Keywords: Predictive control, Embedded control systems, Renewable energy and sustainability
Abstract: The bidirectional buck-boost converter is widely used in applications such as renewable energy systems, electric vehicles, and energy storage systems due to its ability to transfer power bidirectionally. However, ensuring stable operation, fast dynamic response, and high efficiency under a wide range of operating conditions remains a significant control challenge. Traditional methods, such as proportional-integral (PI) control, often struggle to handle the non-linearities and uncertainties inherent in such systems. This paper proposes a Generalized Predictive Control applied to Bidirectional Buck-Boost Converter to address the aforementioned challenges, offering improved performance in terms of dynamic response, stability, and efficiency. The effectiveness of the proposed approach is validated through simulation results conducted in both buck and boost operating modes.

Keywords: Predictive control, Intelligent control systems
Abstract: Pseudo-Linearization-based model predictive control (MPC) using model free control (MFC), which combines data-driven and model-based controls, is proposed to improve the transient response of rapidly changing reference signals. The conventional MFC cannot be applied as an inner loop of the controller with the MPC as an outer loop because the dynamic characteristic of MFC is always equal to one. To solve this problem, a conventional MFC is extended to a model-matching type MFC by adopting a proportional leading type MFC as an inner loop of the proposed method. In addition, virtual reference feedback tuning is introduced to optimize an important parameter of MFC. The experimental results for artificial muscle control with strong asymmetric hysteresis characteristics show that the proposed method improves the transient response by rapidly changing the reference signals. The proposed method can be applied to nonlinear systems and is practical because it does not require system identification or a model structure.

Keywords: Predictive control, Nonlinear systems, Switching systems
Abstract: This paper proposes a data-driven method for modeling and optimizing irrigation processes. It employs a specialized auto-regressive model that considers delays and switching behaviours. In the control architecture, discrete inputs are connected to each irrigation unit, which improves their management individually. The proposed Model Predictive Control (MPC) manages multiple irrigation units, which complies with the actuator operational constraints. This results in a nonlinear mixed-variable optimization problem. To address this, linearization methods are applied. Then, the Gurobi optimizer is used for efficient computation. The results of the simulation using field data show that this approach leads to optimal irrigation schedules.

Keywords: Predictive control, Optimisation, Modelling and simulation
Abstract: Building energy systems pose challenging control tasks when aiming for energy efficient though thermally comfortable control of heating, cooling, and ventilation. Rule based controllers can be easily outperformed by taking system predictions and weather forecasts into account. For model predictive control (MPC), the choice of the cost function is crucial. Here, we show a multi-objective MPC case study and present automated decision making approaches. This reveals the trade-off between cost functions and underlines their importance in MPC control design for building energy systems.

Keywords: Image processing, Embedded control systems, Real-time control
Abstract: Line segment tracker is a fundamental technique in computer vision that follows linear structures in man-made environments, playing a crucial role in Visual Odometry (VO) applications. Although feature-line segments are more efficient than feature-corners in image understanding and geometric inference by enhancing orientation change cues, they face critical challenges such as over-segmentation, occlusions, and ambiguities. In this paper, we address line tracking issues by leveraging feature-line properties. We propose an efficient sparse Lucas-Kanade Optical Flow (OF)-based method to track line segment points. By utilizing the line-fitting model, the anchor, and the gradient properties, we enhance optical flow predictions through three methods: i) an enhanced RANSAC method that uses the segment anchor to reject outliers, ii) a minimization technique that adjusts OF predictions based on anchor direction and gradient, and iii) a flow correction method that integrates both RANSAC and minimization approaches. To meet real-time and embedded constraints, we adopt the Algorithm Architecture Adequacy (A3) methodology for suitable implementation in GPU-based heterogeneous architecture. We also conduct qualitative and quantitative benchmarks in indoor/outdoor environments against state-of-the-art VO front-ends. The results demonstrate the effectiveness of our proposed methods, highlighting the efficiency of tracking line segments via the anchor in real-time at about 50 fps within GPU-based embedded heterogeneous architectures.

Keywords: Modelling and simulation, Discrete-event systems, Multi-agent systems
Abstract: This paper presents the design and implementation of a max-plus algebra-based platform to plan and model the coordinated behaviour of fish robots during underwater surveys. The tool, developed using MATLAB App Designer, features an intuitive Graphical User Interface (GUI) that allows users to define system parameters in terms of travel and exploration timings for the chosen route and input data. The GUI then simulates the temporal evolution of the coordinated tasks through the background creation of a general max-plus linear system. The GUI provides a clear visualisation of the input and output values, enabling seamless integration of mathematical modelling with practical applications. The case studies demonstrate the platform’s capability to make task coordination efficient and highlight potential improvements to advance underwater robotics research.

Keywords: Power systems and smart grid
Abstract: This paper outlines the development of an S-band power amplifier with a GaN HEMT technology transistor to achieve high output power. The methodology employed in this study is the loadpull technique, which aims to determine the optimal impedance for maximizing gain and efficiency. The matching process involved the use of impedance transforming lines in microstrip technology. The design of the Power Amplifier matching networks incorporated two impedance transforming lines. Their characteristics parameters were calculated before moving to simulations on ADS software (Advanced Design System). Through the use of an optimization tool, a successful matching was achieved. The resulting device demonstrated an output power of 42.532 dBm, a maximum gain of 13.532 dB, and a Power-Added Efficiency (PAE) of 56.501%.

Keywords: Renewable energy and sustainability, Neural networks, Modelling and simulation
Abstract: Li-ion batteries are currently one of the preferred storage systems for electric vehicles, due to their durability and high power density. For reliable energy management in these vehicles, a battery management system (BMS) is essential. One of its key parameters is the state-of-charge (SoC), which enables the remaining energy in the battery to be measured, thus guaranteeing its autonomy. This makes accurate estimation of the state-of-charge essential. In this paper, two SoC estimation techniques are implemented. Adaptive Leunberger’s observer (ALO) and a proposed Nonlinear autoregressive exogenous (NARX) model neural network. The performance is evaluated on the US06 driving cycle using mean square error (MSE). The results show that the NARX model offers significantly higher accuracy with 0.0018 than the ALO with 0.6023, confirming the effectiveness of neural networks for SoC estimation.

Keywords: Fuzzy logic and fuzzy control, Power systems and smart grid
Abstract: This paper presents the design and control of a bidirectional power converter for electric vehicle (EV) applications, supporting both Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) operations. To enhance the performance, robustness, and power quality of the system, a fuzzy Proportional-Integral (PI) control strategy is developed. The controller adjusts the converter’s duty cycle based on the deviation between reference and actual signals, with fuzzy logic rules derived from the dynamic behavior of a conventional PI controller. A detailed stability analysis is conducted to establish appropriate tuning criteria and ensure closed-loop robustness. The converter system architecture comprises an AC/DC stage using a Voltage Source Inverter (VSI) and a DC/DC stage based on a Dual Active Full Bridge (DAFB) topology. The proposed control scheme is validated through comprehensive simulations in MATLAB/Simulink under both charging and discharging conditions. The results demonstrate that the fuzzy PI controller significantly improves system stability,reduces harmonic distortion, and ensures effective bidirectional power flow, establishing a reliable framework for future integration into smart grid applications.

Keywords: Nonlinear control, Predictive control, Renewable energy and sustainability
Abstract: This study focuses on designing a robust control method for floating offshore wind turbines (FOWTs), aiming to achieve maximum power generation in Region II while reducing fatigue loads on the wind turbine’s platform. Due to the complex dynamic modeling of FOWTs, a reduced-order model is chosen for controller design. However, this simplified model cannot fully capture the behavior of FOWTs under various conditions. To address this issue, a robust control method based on optimal predictive control (OPC) and super-twisting (STW) sliding mode control is introduced. OPC and STW are integrated to mitigate their respective limitations and to achieve optimal performance with robustness against perturbations. The efficacy of the proposed controller is validated through comparison with reference open-source controller (ROSCO) within the MATLAB/Simulink–OpenFAST environment. The comparison results indicate that the proposed method enhances power generation while effectively mitigating power fluctuations.

Keywords: Distributed systems, Nonlinear systems, Real-time control
Abstract: This work concentrates on the regional optimal control problem for a semilinear parabolic system defined over a spatial domain Omegasubset mathbb{R}^n. The system is driven by a multiplcation of a time control and a semilinear state function at the boundary partial Omega of Omega. And then, it focuses on minimizing the deviation on a subregion omega of Omega between the desired state and the final one at time T , the tracking term all over the time interval [0,T] and the term energy. Next, we demonstrate the existence of a boundary optimal control and characterize it as the solution of an associated optimality system. Moreover, the necessary conditions for optimality are expressed in the form of a variational inequality. Additionally, we explore applications to different sets of controls. An algorithm is derived from this approach and its performance is demonstrated using numerical simulations.