Comparative Performance Analysis of Vector Control, Fuzzy Logic Control, and Adaptive Quantum Fuzzy Logic for Induction Motor Drives in Hybrid Renewable Energy Systems

This paper presents an Adaptive Quantum Fuzzy Logic (ALFQ) control strategy applied to an induction motor (IM) supplied by a hybrid renewable energy system integrating photovoltaic (PV), wind, and thermoradiative (TR) energy sources. Conventional Vector Control (VC) and classical Fuzzy Logic Control (FLC) approaches are not fully developed in this study; instead, their main limitations and performances reported in the literature are used as comparative references in order to focus on the proposed quantum-based control strategy. The main objective is to investigate the dynamic response, robustness against parameter uncertainties, harmonic performance, and adaptability of the proposed ALFQ controller under varying operating conditions. The ALFQ approach combines fuzzy inference mechanisms with quantum-inspired optimization algorithms capable of automatically adjusting the membership functions and minimizing the speed tracking error. Simulation results obtained under MATLAB/Simulink demonstrate that the proposed ALFQ controller significantly improves the dynamic behavior of the induction motor drive. The obtained results show fast response time, quasi-zero overshoot, negligible steady-state error, reduced current ripple, and lower Total Harmonic Distortion (THD). The controller also exhibits excellent robustness against rapid load disturbances, rotor resistance variations, and nonlinear operating conditions. A comparative analysis based on literature results confirms that the proposed ALFQ strategy outperforms conventional VC and classical FLC approaches in terms of tracking accuracy, robustness, harmonic reduction, and global dynamic stability. These results demonstrate the effectiveness of quantum-inspired fuzzy optimization for high-performance induction motor drives supplied by hybrid renewable energy systems.