This paper introduces a brand-new, inspired optimization algorithm (the chaotic billiards optimization (C-BO) approach) to effectively develop the optimal parameters for fuzzy PID techniques to enhance the dynamic response of the solar–hydrogen drive of an induction motor. This study compares fuzzy-PID-based C-BO regulators to fuzzy PID regulators based on particle swarm optimization (PSO) and PI-based PSO regulators to provide speed control in solar–hydrogen, induction-motor drive systems. The model is implemented to simulate the production and storage of hydrogen while powering an induction-motor drive which provides a great solution for the renewable energy storage problem in the case of solar pumping systems. MATLAB/Simulink 2021a is used to simulate and analyze the entire operation. The laboratory prototype is implemented in real time using a DSP-DS1104 board. Based on the simulation and experimental results, the proposed fuzzy-PID-based C-BO has reduced speed peak overshoot by 45.3% when compared to a fuzzy PID based PSO speed regulator and by 68.13% when compared to a PI-based PSO speed controller in the case of a large-scale motor. Additionally, the proposed speed regulator has a 6.1% faster speed rising time than a fuzzy-PID-based PSO and a 9.5% faster speed rising time than a PI–PSO speed controller. It has an excellent dynamic responsiveness value when compared to the other speed regulators.

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.

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 validates the optimal operation for a grid-connected double-fed induction generator (DFIG) in an oscillating water column power plant (OWCPP). This study presents a novel optimization technique called the circulatory system-based optimization (CSBO) approach to develop six adaptive fuzzy logic controllers (AFLCs) with 30 parameters and compare them to chaotic-billiards optimization (C-BO) and genetic algorithm (GA). The proposed controller is also compared with a proportional–integral differential (PID) controller based on a self-adaptive global-best harmony search (SGHS). CSBO-based AFLCs are fully investigated under different scenarios and experimented with using a real-time interface DSP1104. The results of using CSBO–AFLCs revealed a fast time response, fast convergence, less overshoot and minimal error compared with those achieved with C-BO–AFLC, SGHS–PID and GA–AFLC during different case studies. The CSBO-based AFLCs ensure maximum power from the DFIG in an OWCPP and enhance dynamic response with very low errors. The results show that the CSBO shows better power tracking by 25% as compared with C-BO, by 45% when compared with the GA and by 56% when compared with PID. Moreover, the integral absolute errors of six controllers are investigated to demonstrate the feasibility of CSBO–AFLC. The root mean square of the errors of six controllers using CSBO is improved by 68.27% when compared with GA, by 22.57% when compared with C-BO and by 38.42% when compared with PID. These indicators demonstrate the feasibility of CSBO when compared with other algorithms with the same OWCPP.

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.