A Novel Approach of Pi Controller for Speed Regulation of Pmsm by Back Propagated Spiking Neural Network Method based on Slime Mould Algorithm

The efficient control of Permanent Magnet Synchronous Motors (PMSMs) is a critical challenge in modern electrical engineering, particularly for applications in renewable energy, automotive systems, and industrial automation. This study explores advanced control strategies for PMSM speed regulation, focusing on sliding mode control, fuzzy logic, neural networks, and optimization-based techniques. The integration of artificial intelligence methods, such as adaptive fuzzy controllers and neural networks, is emphasized for achieving robust and adaptive performance in dynamic environments. Additionally, novel optimization algorithms, including Particle Swarm Optimization (PSO) and Ant Lion Optimizer, are applied to enhance the design of controllers and improve efficiency, reliability, and system stability. The research further investigates predictive speed control methods and hybrid approaches, highlighting their capacity to address challenges like parameter variations, nonlinearity, and disturbances in PMSM systems. Advanced simulation tools, including MATLAB/Simulink, are employed for the modeling, analysis, and validation of proposed methodologies. Practical implementations in renewable energy systems and electric vehicles demonstrate the feasibility and effectiveness of the studied techniques. The findings reveal that combining artificial intelligence with traditional control methods significantly improves speed regulation and energy efficiency. This work contributes to the advancement of intelligent motor control systems, providing insights into future research directions and real-world applications. The results promise to enhance the performance of PMSMs in high-demand scenarios.