Aissa Benhammou

This paper discusses a novel fuzzy adaptive sliding mode control (FASMC) strategy for a four-wheel-drive (4WD) electric vehicle (EV), incorporating an exponential reaching law (ERL) and a fuzzy adaptive switching gain to enhance speed tracking. The classical SMC technique often suffers from the chattering problem, which can degrade the dynamic control performance of the electric vehicle. To address these challenges, the proposed hybrid controller employs an exponential reaching law to ensure fast convergence and reduced chattering, while a fuzzy logic-adaptation mechanism dynamically adjusts the switching gain to improve robustness against uncertainties and external disturbances. First, the mathematical model of the motor derived for achieving speed regulation using the classical SMC with an exponential reaching law based on indirect-field-oriented control (FOC). Then, the proposed control technique is designed to automatically adjust the ERL gain using a fuzzy logic controller to ensure precise vehicle speed control, optimizing the vehicle's dynamics under varying road conditions. This novel configuration enables the development of a 4WD EV control framework with an optimized controller, serving as the foundation for implementing our proposed study. The results validate the proposed method's superiority, delivering lower chattering, enhanced tracking precision, and greater robustness compared to traditional SMC while adhering to control standards. This control framework presents a viable advancement for 4WD EV motion management, supporting safer, more effective autonomous vehicle technologies.

This study presents a comprehensive energy management strategy designed for an off-grid agricultural microgrid that integrates a PV array, Home Battery Energy Storage (HBES), and Electric Tractor Battery Energy Storage (TBES). The proposed tractor-to-home (T2H) system coordinates power flows among renewable generation, stationary storage, household loads, and the electric tractor’s battery to maximize renewable energy utilization, extend system autonomy, and enhance operational reliability. Key contributions include a dynamic load prioritization mechanism that adaptively manages demand, and a refined Flexible Power Point Tracking (FPPT) algorithm to optimize solar energy harvesting under variable irradiance. The tractor power emulator is incorporated to realistically replicate traction motor dynamics for Processor-in-the-Loop (PIL). Experimental results obtained using the real-time MicroLabBox control platform confirm the effectiveness of the proposed energy management strategy in sustaining energy balance, minimizing battery degradation, and providing reliable support for both agricultural operations and household power needs over extended durations. Furthermore, the system consistently maintains voltage regulation errors below 0.5% and achieves efficiency levels exceeding 96% across all operating modes. Collectively, these findings demonstrate substantial progress toward sustainable electrification for rural farming contexts, offering a robust and environmentally friendly alternative to conventional off-grid energy solutions.

Wind energy conversion systems (WECSs) require robust and efficient control strategies to ensure optimal energy conversion. This study proposes a nonlinear and resilient control approach using a fractional‐order proportional integral‐ and fractional‐order proportional derivative (FOPI–FOPD) controller for direct power regulation of a doubly fed induction generator (DFIG)–based WECS. To meet the control objectives, two cascaded FOPI–FOPD controllers were designed, resulting in 12 parameters requiring precise tuning. To optimize these parameters, the Gazelle optimization algorithm (GOA) was employed, targeting the minimization of key performance‐based cost functions: mean error (ME), mean absolute error (MAE), mean‐square error (MSE), and integral time absolute error (ITAE). These functions integrate dynamic response criteria such as overshoot, rise time, and settling time. Simulation results highlight the effectiveness of the GOA‐tuned FOPI–FOPD controller, particularly when using ITAE as the optimization criterion. The controller significantly reduces power ripples by 86.13% in active power and 75.66% in reactive power. It also improves transient response by reducing rise time by 0.035 ms, settling time by 0.3 ms, and completely eliminating overshoot. Moreover, the proposed strategies lower the current total harmonic distortion (THD) by approximately 21.43% compared to the basic strategy. The proposed ITAE–GOA–FOPI–FOPD technique ensures system stability and enhances performance across various operating conditions.

This study explains the implementation and design of a Fuzzy Logic Controller (FLC) for enhanced speed control in a four-wheel-drive (4WD) electric vehicle (EV). Traditional Proportional-Integral-Derivative (PID) controllers often struggle with the nonlinear dynamics, parameter variations, and external disturbances inherent in EV systems. To address these challenges, we propose an intelligent FLC that leverages heuristic knowledge to provide robust and adaptive control without requiring a precise mathematical model. The controller's performance is evaluated through simulation under various driving conditions, including different road surfaces and sudden load changes. Results demonstrate that the proposed FLC system achieves superior performance compared to a conventional PI controller, exhibiting significantly reduced rise and settling times, minimal overshoot, and enhanced stability, thereby ensuring improved traction, energy efficiency, and driving comfort.

This paper presents the experimental validation of a single-phase Unified Power Quality Conditioner (UPQC) designed to mitigate common power quality issues in low-voltage distribution networks. The system integrates series and shunt active filters to simultaneously compensate for voltage disturbances, suppress harmonic currents, and provide reactive power compensation. A detailed system model is developed to support the control design, enabling effective performance in both transient and steady-state conditions. The implemented control method prioritizes robustness against grid perturbations and nonlinear load variations. Experimental tests, conducted on a hardware prototype under various load and grid conditions, confirm the UPQC’s capability to restore voltage profiles, significantly reduce total harmonic distortion (THD), and maintain stable reactive power compensation. The results validate the practical feasibility and efficiency of the proposed single-phase UPQC solution for enhancing power quality in single-phase distribution systems.

Robotics has become a deeply interdisciplinary field, addressing a broad spectrum of challenges to support human activities across diverse environments on land, in the air, underwater, and even in space. This study focuses on the quadcopter, offering a detailed analysis of its modelling, control strategies, and performance optimisation, particularly when navigating sharp trajectories. Stability plays a crucial role in the effectiveness of quadcopters and other robotic systems. In this context, the research explores how a fractional-order PID (FOPID) controller can enhance stability, especially in systems characterized by high sensitivity. To achieve optimal control, five fractional parameters of the FOPID controller are tuned using three distinct optimisation techniques: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Bonobo Optimization (BO). The comparative evaluation reveals that the FOPID controller, when optimized particularly with the BO method, demonstrates superior control performance. It notably eliminate overshoot and improves both settling and rise times by more than 30% under consistent test conditions, outperforming conventional controllers cited in the literature.

This paper introduces a comprehensive and modular control strategy for a quadcopter employing six decoupled fuzzy logic controllers, each dedicated to controlling one degree of freedom: roll, pitch, yaw, altitude, and horizontal positions (x, y). The key novelty of this work lies in the integration of a novel multi-objective Self-Adaptive Bonobo optimizer to optimize eighteen controller gains simultaneously, leveraging Pareto-front principles for the first time in this context to effectively balance tracking accuracy with control effort. The proposed controllers robustly handle the nonlinear and coupled dynamics of the quadcopter while maintaining resilience against disturbances and model uncertainties. The obtained results validate the efficacy of this approach, demonstrating significant improvements, including up to a 51% reduction in root mean square error and 76% reduction in mean absolute error in lateral motion over baseline configurations, without compromising altitude stability and with enhanced response times compared to random gains selection. Furthermore, the control scheme is validated via real-time implementation on a dSPACE 1202 Processor-in-the-Loop platform under aggressive trajectory conditions. Finally, a novel conceptual framework is proposed for deploying these optimized fuzzy controllers directly on embedded flight hardware under aggressive trajectories, creating efficient autonomous onboard control and representing a practical step towards real-world Unmanned Aerial Vehicles applications.

Electric vehicles (EVs) are a sustainable and efficient transportation choice, offering zero emissions, lower operating costs, and advanced performance features like instant torque and regenerative braking. They promote energy independence, improve urban livability, and support the global shift toward cleaner, renewable energy-powered mobility, making them a future-proof investment. The electric motor is a critical component in electric vehicles (EVs), the importance of which lies in its high efficiency, instant torque delivery, and smooth operation, which enhances performance and energy use. This paper focuses on a two-wheel drive electric vehicle (TWD EV) configuration powered by an energy storage battery system (ESBS), driven by two permanent magnet synchronous motors (PMSMs), and controlled using direct torque control with space vector modulation (DTC-SVM). fractional-order proportional integral derivative (FOPID) controllers, optimized via the grey wolf optimizer (GWO) algorithm, are implemented for precise speed control of the PMSMs. An electronic differential (ED) is incorporated to ensure vehicle stability, safety, and performance. The simulation results show that the proposed GWO-FOPID controller gave super results by reducing electromagnetic torque overshoot by 33%, improves torque settling time by 55%, and achieves the lowest electromagnetic torque ripple of approximately ±1 Nm compared to conventional DTC-SVM and GWO-PID approaches. Additionally, it optimized speed overshoot and undershoot by 44%, significantly enhancing system performance, responsiveness, and drive smoothness. This novel combination of fractional-order control, metaheuristic optimization, and electronic differential integration marks a meaningful advancement in high-precision and efficient control for 2WD EVs.

This work compares four control strategies for a photovoltaic-diesel generator-hybrid energy storage system, focusing on classical proportional-integral (PI) versus nonlinear super-twisting-sliding-mode-control (ST-SMC). The performance is evaluated using root mean square error (RMSE) and mean absolute error (MAE) metrics. The PI controller provides a better DC-Bus voltage response under pulse conditions, achieving an RMSE of 2.48 and MAE of 0.61. In contrast, the ST-SMC excels in battery current tracking, with an RMSE of 27.13 and MAE of 7.10, leading to reductions of 85% in RMSE and 81% in MAE compared to PI. For supercapacitor current control, ST-SMC shows an RMSE of 6.79 and MAE of 0.96, significantly outperforming PI's RMSE of 139.62 and MAE of 24.88, resulting in reductions of 95% and 96%, respectively. Both controllers perform similarly under load mismatch conditions, with ST-SMC slightly better at MAE (0.07 vs. 0.08). Overall system power losses yield similar results for both controllers (RMSE ∼14.58, MAE ∼9.33). Such results reflect the strength of ST-SMC, regarding robustness and precision, hence quite suitable for high-precision challenges. Considering the novelty of outstanding improvements in current control of the battery and SCs, the ST-SMC is a robust option that fits widely for high stability and reliability systems.

In recent years, quadrotors have emerged as essential roles in robotics, and their diversity and usefulness emphasize their importance. This research work presents an in‐depth analysis of a quadcopter in terms of modeling, control, and optimization, where central to the operation of quadcopters and all robotics systems is the idea of stability response. This paper discusses the possibility of providing quadcopter stability by demonstrating the impact of the fractional controller in sensitive systems. The five fractional parameters for each engine are also improved using the Bonobo Optimization (BO) algorithm. The optimized results in this paper are compared with the algorithms used, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO). The fractional‐order proportional integral derivative (FOPID) controller has greater control power compared to its classic counterpart, PID control, as it provided improvement in minimizing overshoot by 90%, and it showed great improvement in settling and rising times using GWO 25% and BO 50% with some superiority of BO. By examining both the advantages and constraints inherent in these methodologies, we seek to advance the field forward, promoting more breakthroughs in this crucial area.