Adaptive Control Using RBF Neural Networks Integrated with PID Control

This article primarily introduces an adaptive control methodology that integrates RBF (Radial Basis Function) neural networks with PID (Proportional-Integral-Derivative) control. In the field of adaptive control, both RBF neural networks and PID control represent crucial technologies. The RBF neural network autonomously adjusts controller parameters by learning input-output relationships through its hidden layer neurons with Gaussian activation functions, enabling system adaptation to dynamic changes. PID control, as a classical control approach, regulates controller output by calculating error differences between actual and desired outputs using proportional, integral, and derivative terms. By combining these two methodologies, we leverage their complementary strengths to achieve more stable and precise adaptive control systems. This integration typically involves using the RBF network for online parameter tuning of the PID controller through gradient descent optimization. The article elaborates on the fundamental principles and applications of both RBF neural networks and PID control, with code examples demonstrating how to implement the coupling mechanism using matrix operations for weight updates and error backpropagation. Furthermore, we explore their synergistic interaction mechanisms for achieving adaptive control objectives in complex systems.