RBF Neural Network Control Based on Inverted Pendulum Model

In view of the limitation that it is difficult to accurately control the nonlinear link in the inverted pendulum system control, this paper introduces the use of neural network to approximate the nonlinear link to solve the problem that the controlled object cannot be accurately controlled. This paper mainly uses neural network RBFNN (Radial Basis Function Neural Networks) and modern control theory to control the inverted pendulum system. According to the inverted pendulum model, the neural network structure control algorithm is studied, and the improved RBF neural network algorithm is used to approximate the nonlinear link of the inverted pendulum model. The control parameters of the inverted pendulum structure control algorithm are optimized. The results obtained by MATLAB simulation show that the system performance is better under neural network control, and the mapping relationship between inverted pendulum state and execution action can be established, which increases the robustness and adaptability of system control.