Discrete RBF Adaptive Robust Sliding Mode Network Control Method for High-speed Trains

Abstract

Objective To achieve reliable control of traction and braking key systems in trains while suppressing the impact of communication delay on train control, a discrete RBF (radial basis function) adaptive robust sliding mode control (abbreviated as ARSMC) method is proposed for critical network systems in high-speed trains. Method The traction and braking network systems in high-speed trains are introduced. An ARSMC method considering fractional delay terms is designed, and its stability is analyzed. The proposed discrete RBF ARSMC method is jointly simulated and analyzed using configuration software on a testing platform. Result & Conclusion The delay in discrete systems is divided into integer and fractional terms. By fully considering the fractional delay terms, the sliding mode traction and braking force with delay compensation under discrete approximate law is derived. The unknown nonlinear function in which is accurately approximated using an RBF neural network with adaptive adjustment characteristics. To suppress strong disturbances encountered during train operation, the sliding mode traction and braking force based on disturbance observer is integrated into the model to enhance its disturbance rejection performance. The proposed discrete RBF ARSMC method demonstrates superior stability and responsiveness compared to other control methods, exhibiting more ideal delay compensation effect and robust performance.

Keywords

• high-speed train
• sliding mode network control
• rbf neural network