Mohamed Elshahat Dessouki

This study presents an experimental validation of an adaptive fuzzy logic controller (AFLC) for solar-driven proton exchange membrane (PEM) hydrogen production under dynamic operating conditions. The proposed solar-driven PEM hydrogen production system was accurately modeled and investigated under various operating conditions using the MATLAB/Simulink simulation platform. In order to evaluate the effectiveness of the proposed controller, the AFLC strategy was experimentally investigated using a dSPACE DS1104 platform and compared with conventional Proportional–Integral (PI) and Fuzzy Logic Controller (FLC) approaches in terms of tracking accuracy, transient response, overshoot suppression, current ripple minimization, and overall hydrogen production stability. Under step-change solar irradiance conditions, the proposed AFLC exhibited superior MPPT performance, achieving improvements of 34.32% and 84.26% in power-tracking accuracy compared with the conventional FLC and PI controllers, respectively. The developed control architecture employs real-time feedback from electrolysis to regulate the duty cycle of the DC–DC converter controlled by maximum power point tracking (MPPT), ensuring precise power delivery to the PEM electrolysis despite fluctuating solar irradiance. Additionally, the solar hydrogen production system’s performance indices were computed. These indices are intended to assess the viability of the AFLC in comparison to the PI and FLC under the same solar irradiance conditions.

This research article introduces a novel optimization strategy for fuel cell electric vehicles (FCEVs) in order to reduce the integral square error to enhance dynamic performance. African vulture optimizer algorithm (AVOA) improves a speed fuzzy logic controller's (FLC) internal controller settings. The AVOA is renowned for its simplicity in implementation, and low demand on computational resources. The speed drive of FCEV is investigated using MATLAB/Simulink 2023a. The results of FLC-AVOA provide lower settling time, lower overshoot, lower undershoot, and high dynamic response when compared to FLC and proportional-integral (PI) controllers designed using genetic algorithm (GA). The FLC-AVOA reduced the rising time for speed dynamic response by 2.31% and the maximum peak overshoot by 55.23% as compared to FLC-GA.

This article presents a second-order adaptive fuzzy logic controller (SO-AFLC) to improve the performance of a grid-connected double-fed induction generator (DFIG) in an oscillating water column power plant (OWCPP). The proposed SO-AFLC was used to improve the maximum power point tracking (MPPT), DC link voltage stability, and reactive power tracking for the DFIG oscillating water column power plant. The SO-AFLC reduces oscillations, overshooting, and mean square error. The SO-AFLC improved the mean square error by 40.4% in comparison to the adaptive fuzzy logic controller (AFLC) and by 84.9% in comparison to the proportional–integral differential controllers (PIDs). To validate the simulation results, an experimental investigation was performed on the Dspace DS 1104 control board. The SO-AFLC shows a faster response time, reduced undershooting, lower peak overshooting, and very low steady-state error in terms of DC link voltage, rotor speed, and maximum power point tracking. Moreover, the integral absolute error (IAE) index of the oscillating water column turbine was calculated. This index is meant to evaluate the SO-AFLC’s feasibility against the PID and AFLC under the same wave conditions.

This paper introduces a second-order adaptive fuzzy logic controller (SO-AFLC) to enhance the characteristics of a doubly fed induction generator (DFIG) inside a grid-tied wind power plant (WPP). SO-AFLCs were utilized to maximize the output of the DFIG wind power plant (WPP) and improve dynamic responsiveness with extremely low mean square error. When comparing the mean square error of SO-AFLC with proportional-integral controllers (PI) and adaptive fuzzy logic controllers (AFLC), the reductions are 87.38% and 40.39%, respectively. This controller prevents overshoots and oscillations. DFIG wind power plant is modeled and simulated using Matlab/Simulink package. Under the unit step wind speed profile, SO-AFLC improved the steady-state error in the Cp waveform by 63.25% compared with the PI controller and 13.12% compared with AFLC. DSpace1104 is used to conduct an experimental investigation to validate the simulation results. In addition, realistic data from the wind farm at RAS Ghareb in the Gulf of Suez, Egypt, are used in this study to achieve more realistic results. Compared to those obtained with PI and AFLC, the results obtained using SO-AFLCs showed fast time response, high convergence rate, reduced peak overshoot, less undershoot, and low steady-state error in terms of power coefficient of the turbine, DC link voltage control, and rotor speed tracking. In addition, a wind turbine performance index based on gross system integral absolute error (IAE) is provided. This index is used to illustrate the SO-AFLC methodology’s viability compared to AFLC and PI under the same wind turbine conditions.

This study describes a new optimization approach for three-phase induction motor speed drive to minimize the integral square error for speed controller and improve the dynamic speed performance. The new proposed algorithm, African vulture optimizer algorithm (AVOA) optimizes internal controller parameters of a fuzzy like proportional differential (PD) speed controller. The AVOA is notable for its ease of implementation, minimal number of design parameters, high convergence speed, and low computing burden. This study compares fuzzy-like PD speed controllers optimized with AVOA to adaptive fuzzy logic speed regulators, fuzzy-like PD optimized with genetic algorithm (GA), and proportional integral (PI) speed regulators optimized with AVOA to provide speed control for an induction motor drive system. The drive system is simulated using MATLAB/Simulink and laboratory prototype is implemented using DSP-DS1104 board. The results demonstrate that the suggested fuzzy-like PD speed controller optimized with AVOA, with a speed steady state error performance of 0.5% compared to the adaptive fuzzy logic speed regulator’s 0.7%, is the optimum alternative for speed controller. The results clarify the effectiveness of the controllers based on fuzzy like PD speed controller optimized with AVOA for each performance index as it provides lower overshoot, lowers rising time, and high dynamic response.

Mobile robots are typically depending only on robot kinematics control. However, when high-speed motions and highly loaded transfer are considered, it is necessary to analyze dynamics of the robot to limit tracking error. The goal of this paper is to present a new algorithm, chaotic-billiards optimizer (C-BO) to optimize internal controller parameters of a differential-drive mobile robot (DDMR)-based dynamic model. The C-BO algorithm is notable for its ease of implementation, minimal number of design parameters, high convergence speed, and low computing burden. In addition, a comparison between the performance of C-BO and ant colony optimization (ACO) to determine the optimum controller coefficient that provides superior performance and convergence of the path tracking. The ISE criterion is selected as a fitness function in a simulation-based optimization strategy. For the point of accuracy, the velocity-based dynamic compensation controller was successfully integrated with the motion controller proposed in this study for the robot's kinematics. Control structure of the model was tested using MATLAB/Simulink. The results demonstrate that the suggested C-BO, with steady state error performance of 0.6 percent compared to ACO's 0.8 percent, is the optimum alternative for parameter optimizing the controller for precise path tracking. Also, it offers advantages of quick response, high tracking precision, and outstanding anti-interference capability.