Brahim Brahmi

This paper introduces a novel second‐order sliding mode control (SOSM) strategy that employs a nonlinear terminal model‐based switching surface to achieve precise and reliable trajectory tracking for a two‐DoF upper‐limb exoskeleton robot. By integrating the robot's dynamic model into the switching manifold, the proposed approach simplifies the torque control law and effectively decouples chattering between the joint axes. The inclusion of a nonlinear terminal sliding term endows the closed‐loop dynamics with terminal‐type behavior, leading to fast convergence of the tracking errors and improved transient performance and disturbance attenuation. Unlike classical sliding mode control (SMC), which can suffer from significant discontinuities and stress actuators, the proposed SOSM–NTSM scheme provides smoother control actions. In line with high‐order sliding‐mode results, it achieves a significant reduction of chattering and asymptotic convergence of the model‐based sliding variable in the presence of bounded modeling uncertainties and external disturbances, together with locally exponential convergence of the tracking errors once sliding is established. Validations and a comparative study conducted on the iTbot exoskeleton robot demonstrate significant improvements in tracking accuracy, torque smoothness, fast convergence, and overall robustness. This makes the proposed approach particularly well suited for ensuring safe and comfortable human‐robot interactions in rehabilitation scenarios.

The autonomous landing of a helicopter UAV on a moving marine surface vessel (USV) presents significant challenges in rescue missions, particularly when cyber‐attacks compromise the communication channel. This paper addresses the secure control problem of helicopter landing on a USV under false data injection (FDI) attacks affecting the one‐way communication link from the USV to the helicopter. To mitigate the adverse effects of such attacks on transmission integrity, data privacy, and landing stability, a novel resilient masking approach is proposed. Specifically, virtual states are introduced and interconnected with the physical states of the USV such that the landing mission is guaranteed despite unknown FDI cyber‐attacks. The dynamics of these virtual states act as a dynamical output mask, mapping the physical states of the USV to virtual states that are transmitted through the communication channel. To further enhance efficiency and reduce communication burden between the controller and the actuator, an event‐triggered control (ETC) mechanism is incorporated, whereby control updates are transmitted only when a state‐dependent triggering condition is satisfied. This strategy significantly reduces communication cost and control execution effort while preserving closed‐loop stability and performance. The helicopter UAV decodes the masked signal via an estimator and is guided toward the USV landing pad using an optimal control strategy based on a reinforcement learning framework with an identifier‐actor‐critic architecture. Furthermore, position‐state constraints are addressed by introducing a universal nonlinear transformation that unifies constrained and unconstrained cases into a single framework. Using Lyapunov stability theory, the boundedness of the actor‐critic adaptive laws is rigorously verified. Numerical simulations are carried out to validate the effectiveness of the proposed scheme.

Reliable legged locomotion in unstructured environments requires the integration of fast local response, terrain-aware gait adaptation, and longer-horizon navigation. In many existing systems, these functions are implemented in separate modules, which weakens real-time interaction between low-level control and high-level planning. This paper presents a Hierarchical Perception-Action Coupling (HiPAC) framework organized as a three-level control hierarchy supported by a sensory layer embedded in the onboard robot controller, operating at different time scales. The framework comprises a reactive layer for immediate terrain response and gait execution, a context layer for local environmental recognition and locomotion modulation, and a planning layer for cognitive-map construction, path generation, and replanning. The sensory layer acquires exteroceptive and proprioceptive measurements and forwards them to the higher layers through a lightweight topological representation. In HiPAC, the layers exchange compact topological representations instead of dense raw sensory data, which keeps memory and computation bounded. The framework is evaluated in physics-based simulation on a quadruped robot across flat, rough, and multi-story environments, and is further demonstrated in real–robot deployment. The results show that the reactive layer maintains response times below 30 ms, the proposed topological attention mechanism reduces processing cost by up to 70% relative to uniform-density representations, and local environment reconstruction remains below 18 kB during extended exploration. These findings show that HiPAC provides an efficient architecture for combining real-time terrain response, local adaptation, and global navigation in legged robots.

This paper presents a novel bio-inspired fluid dynamics framework that unifies path planning and control within a single continuous navigation process. Unlike conventional approaches that separate trajectory generation and execution, the proposed method models the robot as a particle immersed in an artificial fluid field, where the goal acts as a sink and obstacles modify the flow to produce collision-free motion. To ensure global optimality and eliminate local minima traps, the framework incorporates a sampling-based enhancement that evaluates multiple trajectories within high-flow regions and selects the optimal path using graph-based optimization. A fluid-based control law directly converts the velocity field into robot motion commands, enabling seamless integration between planning and execution. Theoretical stability is established using Lyapunov analysis, guaranteeing convergence to the goal. Extensive experiments on a Pioneer P3-DX robot demonstrate that the proposed approach achieves execution speeds 1.5 to 9.7 times faster than A*, PRM, and RRT*, while producing paths 3.6% to 29.5% shorter. Furthermore, the unified framework provides smooth and accurate motion with tracking errors within ±0.1 m. These results confirm that the proposed method improves path quality, computational efficiency, and real-time navigation performance.

Human–robot interaction (HRI) remains a critical research area, particularly in assistive robotics, where intuitive, safe, and adaptive collaboration with human users is essential for real-world deployment. Despite significant advances in impedance-based and adaptive control strategies, many existing approaches rely on full-state measurements, exhibit limited capability in handling user intent, and lack adaptability to unstructured environments. This paper addresses these challenges by proposing a novel sensorless control architecture that integrates an optimal Linear Quadratic Regulator (LQR), a nonlinear force observer, and a robust sliding mode controller with nonlinear model-based switching function. The primary objective is to achieve accurate trajectory tracking while minimizing user-applied interaction forces without requiring force or velocity sensors. The proposed controller dynamically generates reference trajectories through a human-cooperative LQR paradigm that penalizes both robot effort and human torque, enabling adaptive behavior based on inferred user intent. A nonlinear observer estimates interaction torques using only joint position measurements, facilitating intent inference and real-time impedance adaptation. These estimates are subsequently incorporated into the construction of model-based switching manifolds within the sliding mode controller, enhancing chattering mitigation and control decoupling under uncertainty and partial state observability. Theoretical analysis establishes global asymptotic stability, and comprehensive simulations conducted on a 2-DOF rehabilitation robot validate the proposed approach under multiple disturbance scenarios. Compared to conventional impedance control strategies, the proposed method demonstrates improved accuracy, robustness, and energy efficiency, highlighting its potential for deployment in sensor-limited, human-in-the-loop applications such as prosthetics, exoskeletons, and adaptive rehabilitation robotics.

This paper aims to improve the effectiveness of an isolated water pumping system (IWPS) based on parallel wind turbine powertrains by using a nonlinear Integral Backstepping Controller (IBSC). The powertrain system with parallel Permanent Magnet Synchronous Generators (PMSGs) is used to ensure the continuity of the operation and to optimise the efficiency of generators. The effective wind speed of a wind turbine is estimated using an Extended Kalman Filter (EKF) instead of using anemometers. Moreover, the Tip Speed Ratio (TSR) method based on an Adaptive Fuzzy Logic Controller (AFLC) is performed to apply the MPPT algorithm. To evaluate the efficiency of the EKF estimator, the covariance and correlation coefficient between the estimated and measured wind speeds are computed for Ts = 1 s, while the AFLC and nonlinear IBSC controllers are evaluated in comparison to basic PI controllers. For the wind speed estimation, the covariance and correlation coefficient results demonstrate that the EKF estimator has high accuracy with a 98% similarity between the estimated and measured wind speeds. For the power extraction, the TSR based on the adaptive FLC controller can extract slightly more wind energy than the PI controller. For tank level control, the nonlinear IBSC controller improves the efficiency of the wind turbine water pumping system by reducing the DC voltage surge and the overshoot of water level observed with the PI controller.

Robotic-assisted therapy is an increasingly vital approach for upper-limb rehabilitation, offering consistent, high-intensity training critical to neuroplastic recovery. However, current control strategies often lack robustness against uncertainties and external disturbances, limiting their efficacy in dynamic, real-world settings. Addressing this gap, this study proposes a novel control framework for the iTbot—a 2-DoF end-effector rehabilitation robot—by integrating differential flatness theory with a derivative-free Kalman filter (DFK). The objective is to achieve accurate and adaptive trajectory tracking in the presence of unmeasured dynamics and human–robot interaction forces. The control design reformulates the nonlinear joint-space dynamics into a 0-flat canonical form, enabling real-time computation of feedforward control laws based solely on flat outputs and their derivatives. Simultaneously, the DFK-based observer estimates external perturbations and unmeasured states without requiring derivative calculations, allowing for online disturbance compensation. Extensive simulations across nominal and disturbed conditions demonstrate that the proposed controller significantly outperforms conventional flatness-based control in tracking accuracy and robustness, as measured by reduced mean absolute error and standard deviation. Experimental validation under both simple and repetitive physiotherapy tasks confirms the system’s ability to maintain sub-millimeter Cartesian accuracy and sub-degree joint errors even amid dynamic perturbations. These results underscore the controller’s effectiveness in enabling compliant, safe, and disturbance-resilient rehabilitation, paving the way for broader deployment of robotic therapy in clinical and home-based environments.

Robotic-assisted therapy is an increasingly vital approach for upper-limb rehabilitation, offering consistent, high-intensity training critical to neuroplastic recovery. However, current control strategies often lack robustness against uncertainties and external disturbances, limiting their efficacy in dynamic, real-world settings. Addressing this gap, this study proposes a novel control framework for the iTbot—a 2-DoF end-effector rehabilitation robot—by integrating differential flatness theory with a derivative-free Kalman filter (DFK). The objective is to achieve accurate and adaptive trajectory tracking in the presence of unmeasured dynamics and human-robot interaction forces. The control design reformulates the nonlinear joint-space dynamics into a 0-flat canonical form, enabling real-time computation of feedforward control laws based solely on flat outputs and their derivatives. Simultaneously, the DFK-based observer estimates external perturbations and unmeasured states without requiring derivative calculations, allowing for online disturbance compensation. Extensive simulations across nominal and disturbed conditions demonstrate that the proposed controller significantly outperforms conventional flatness-based control in tracking accuracy and robustness, as measured by reduced mean absolute error and standard deviation. Experimental validation under both simple and repetitive physiotherapy tasks confirms the system's ability to maintain sub-millimeter Cartesian accuracy and sub-degree joint errors even amid dynamic perturbations. These results underscore the controller’s effectiveness in enabling compliant, safe, and disturbance-resilient rehabilitation, paving the way for broader deployment of robotic therapy in clinical and home-based environments.