S-function in RBF Neural Network Tuned PID Simulink Model

This model demonstrates the implementation of an S-function for RBF neural network-based PID controller tuning in Simulink. The S-function serves as a custom block that encapsulates the RBF neural network algorithm, which dynamically adjusts PID parameters (proportional, integral, and derivative gains) to optimize system performance. The implementation typically involves defining the S-function structure with initialization, output calculation, and update methods, where the RBF network learns system characteristics through gradient descent adaptation to minimize control error. PID controllers represent classical control algorithms that regulate system output by processing error signals between desired and actual values. In this Simulink model, the S-function efficiently handles nonlinear system simulation by implementing the RBF network's hidden layer with Gaussian activation functions and output layer for PID parameter adjustment. Key implementation aspects include defining the network topology, learning rate parameters, and real-time weight update mechanisms within the S-function callback functions. By tuning the PID parameters through the S-function integration, engineers can achieve better understanding of system dynamics and enhance control performance for complex nonlinear systems. The model provides valuable insights for control system design, particularly in applications requiring adaptive control strategies and neural network implementation within MATLAB/Simulink environments.