RBF Neural Network-Based PID Tuning Simulink Simulation Model

I have been recently studying the RBF neural network-based PID tuning Simulink simulation model, which involves implementing radial basis function networks to dynamically adjust PID controller parameters. This research topic proves highly fascinating with significant potential, where RBF neural networks employ Gaussian activation functions and weight adaptation algorithms to optimize PID gains (Kp, Ki, Kd) through gradient descent methods. The Simulink simulation environment allows for constructing control system block diagrams with embedded MATLAB functions for neural network implementation, enabling thorough validation of method effectiveness through performance indicators like rise time and overshoot analysis. During research, I encountered intriguing challenges including RBF network parameter selection (spread constant, center vectors) and tuning process optimization using iterative learning algorithms. Through in-depth study and experimental simulations involving code implementation for real-time parameter adjustment, I believe we can achieve satisfactory results contributing to advanced control system development.