One-parameter Direct Robust Adaptive Train Tracking Control Based on Radial Basis Function Neural Network

Abstract

Aiming at the uncertainty of high-speed train model and time-varying nonlinear systems with external disturbances, this paper proposes a single-parameter direct robust adaptive algorithm based on radial basis function (RBF) neural network for train Tracking control. Based on the characteristics of RBF neural network, a single parameter direct robust adaptive controller is designed for train tracking. First, a single particle train model with external disturbances is proposed; Then based on the single particle train model, based on the RBF neural network's adaptive control closed-loop system, a single parameter direct robust adaptive controller based on the RBF neural network is designed. It can track the position and speed of the train better in the presence of external disturbances. The stability of the closed-loop system was analyzed based on Lyapunov method, and the rationality of the controller design was proved. Finally, in combination with simulink, the CRH2 train is used as a simulation object to simulate the train position and speed, and the errors are analyzed.