Ahmed Said Nouri

This paper presents a robust Fuzzy Logic Parameter Tuning‐based Fractional Reaching Law Sliding Mode Control strategy designed for lower‐limb rehabilitation exoskeletons. The proposed control scheme integrates the robustness of fractional‐Reaching law sliding mode control (FRSMC) with the adaptability of fuzzy logic, optimizing the trade‐off between torque generation and trajectory tracking. This work originally applies fractional‐order dynamics to the sliding mode reaching phase, where the non‐integer differentiation is effectuated in a fuzzy‐adaptive manner, which significantly enhances the management of dynamic behavior in the exoskeleton. This fusion of robust controllers achieves an optimal balance between transient and steady‐state performance by significantly reducing torque peaks while maintaining a rapid transient response, thus ensuring smooth operation throughout the rehabilitation task. A comprehensive stability analysis confirms the asymptotic convergence of the sliding function and the elimination of tracking errors, demonstrating the controller's efficiency. Simulation results on a three degrees‐of‐freedom (DOF) lower‐limb exoskeleton validate the strategy's effectiveness of the proposed controller.

Robotic manipulators are indispensable in various industrial applications, yet their performance is often compromised by parametric uncertainties, including variations in load, friction, and external disturbances. To overcome these challenges, this paper introduces a robust adaptive strategy with fractional differentiation’s for sliding mode control (AFO-SMC). While traditional sliding mode control (SMC) is renowned for its robustness, it suffers from chattering issues that degrade system efficiency. Integrating fractional-order calculus with SMC enhances control flexibility and robustness under uncertain conditions. The adaptive mechanism within the AFO-SMC dynamically adjusts control parameters, ensuring consistent tracking performance and stability despite significant variations on system parameters with external disturbances. The reliability of the proposed control scheme is validated through a comprehensive stability analysis, which is divided into two main parts: the first part ensures that the system state reaches and remains on the sliding surface, while the second stability analysis study focuses on eliminating the trajectory tracking error of the robotic arm. Simulation results demonstrate that AFO-SMC significantly outperforms conventional control methods, achieving high precision and robustness in the presence of uncertainties.

This paper investigates the application of Static VAR Compensators (SVCs) with neural network control to enhance power system grid stability, particularly in multi-source energy systems. SVCs, as Flexible alternating current Transmission Systems (FACTS) devices, are crucial for reactive power compensation and voltage regulation. The study models and simulates an SVC controlled by a neural network in MATLAB/Simulink, assessing its performance under three-phase fault conditions. The fault a 3-phase to-ground short circuit fault is introduced at location in close proximity to the wind energy sults demonstrate that the proposed control scheme effectively reduces system oscillations and improves dynamic response, leading to faster fault recovery and enhanced overall grid stability. The superior dynamic performance of the SVC-based neural network controller confirms its potential for improving power system resilience.

Rehabilitation robots, particularly lower-limb exoskeletons, are transforming healthcare by assisting individuals with mobility impairments. This study introduces a novel Sliding Mode Control (SMC) system based on a fractional-order reaching law, designed to enhance control performance and robustness. The proposed approach effectively manages the exoskeleton’s dynamic behavior, particularly during the transient regime, by reducing initial torque energy demand during start-up, ensuring precise trajectory tracking, and prioritizing patient safety and comfort. The method’s effectiveness is validated through MATLAB simulations and supported by a rigorous dual stability analysis, demonstrating asymptotic and finite-time convergence of the system in the reaching and sliding phases. A Comparison study with traditional SMC techniques proves that the FO-RL-SMC significantly improves energy efficiency during the transient phase and the overall dynamical behavior of the system. These results highlight the potential of the proposed FO-RL-SMC system to advance the performance of rehabilitation robots, emphasizing its value in addressing complex control challenges and improving patient outcomes.

In the current work, additive Fault-Tolerant Control (FTC) for wind turbine system is addressed. To design the additive control, two steps must be taken: first, the sensor fault amplitude must be accurately estimated using an Unknown Input High Gain Observer (UIHGO) with variable design parameters; second, the additive FTC law must be generated and added to the system's nominal control; the Maximum Power Point Tracking (MPPT) must be taken into account while the formulation of the nominal control law. The expression of additive control is equal to zero in the absence of a fault, while the additive control law must depend on the fault term. The method turns into an active fault-tolerant controller (AFTC) that can immediately adjust the FTC to compensate for the fault effect because the observers offered a reliable and accurate online fault estimation. The validity of the implemented controller is confirmed by the simulation results, which show how effective the recommended controller is even in the presence of faults.

This paper deals with diagnosis and fault detection in a wind conversion system based on Unknown Input Sliding Mode Observer(UISMO). Actually, with the increasing rate of use and exploitation of wind turbine systems as proper and environment friendly energy source, the need of an accurate strategy able to ensure process reliability is well demanded both from industrial community and academic researchers. Wind Energy Converter Systems (WECS) are often operating in harsh conditions and may be subject to a variety of faults affecting their reliability and may lead to severe damage. To avoid such uncomfortable situations, a diagnosis strategy is proposed in this work that focus on fault estimation and detection for such a process. Considered as a nonlinear system subject to failure in presence of perturbations. These later are assumed to be unknown but bounded witch relies with the real process dynamics. An LMI based strategy is adopted to ensure observer stability and a residue generation logic is employed to decide whether a fault has effectively occurred or no. Some fault appearance scenarios are then studied in order to establish the observer capabilities to estimate and detect WECS faults. The proposed algorithm was successfully applied, in simulation, on a WECS workbench borrowed from [1] and demonstrated interesting results while future works will concerns its robustness test against noise and external perturbations.

The stability of the electrical network is considered a major challenge in the development of energy systems based on various sources. This research provides a comparison of the dynamic performance of FACTS devices such as STATCOM. These techniques, which are integrated stability devices with a multi-source power system, are used. The neural network technology unit is used to control FACTS devices to enhance the performance of power sources under abnormal and different conditions. Testing is conducted under conditions of three-phase short circuit to ground at different locations in the system. MATLAB/Simulink is used for modeling and simulation. The obtained results demonstrate the impact of the control unit based on STATCOM in reducing system oscillations and improving dynamic system performance during the post-fault period. The confirms the superior dynamic performance and quick fault recovery of the control unit.