RBFNN-Based Fractional-Order Control of High-Speed Train With Uncertain Model and Actuator Failures

Abstract

In this paper, the position/velocity tracking control problem of high speed train(HST) is investigated with considering some inevitable factors such as the input nonlinearity due to different notches of traction/braking forces, aerodynamic resistance, in-train force, external disturbance and unknown actuator failures, which lead to the uncertainty and nonlinearity of HST. Aiming at the system characteristics of HST, a set of integer-order control methods based on the excellent approximation ability of Radial Basis Function Neural Network(RBFNN) are established firstly, and motivated by them, a kind of RBFNN-based fractional-order control methods are proposed by utilizing the genetic attenuation properties of fractional calculus(FC) in order to improve the control performance in the work. It should be pointed out that all the developed methods are able to deal with uncertainties and nonlinearities as well as actuator failures without the need for any “trail and error” process. The feasibility and effectiveness of the proposed control methods are verified by Lyapunov theoretical analysis and numerical simulation studies. Besides, the control performance of integer-order control system and fractional-order control system is compared and analyzed, and the results show that the fractional-order control system is superior.