Optimizing Neuro-Fuzzy Network Controllers Using PSO Algorithm with MATLAB Implementation

This research employs MATLAB to implement Particle Swarm Optimization (PSO) for optimizing neuro-fuzzy network controllers, followed by performance comparisons with traditional PID controllers and standard fuzzy controllers. The implementation involves designing the PSO algorithm to tune neuro-fuzzy parameters (including membership functions and rule weights) through iterative optimization processes. Key MATLAB functions like "particleswarm" from Global Optimization Toolbox will be utilized for parameter tuning, while Fuzzy Logic Toolbox functions support neuro-fuzzy system development. Performance evaluation metrics such as settling time, overshoot, and steady-state error will be analyzed through simulation studies. The comparative analysis will demonstrate PSO's effectiveness in enhancing controller adaptability and dynamic response characteristics. This investigation provides valuable insights into PSO's application in intelligent control systems and establishes practical guidelines for advanced controller development.