Implicit generalized predictive control method for high-speed train

Abstract

The running process of the train which can high speed running is a typical nonlinear process, and with the higher and higher velocity of the train which can be driven extremely fast, the characteristics of the nonlinear will be stronger than before, which leads to higher requirements for the design of the “Automatic Train Operation” driving system. Aiming at the characteristics of high-speed train running speed fast, complex operation process and strong nonlinearity, the mathematical model establishment and the speed tracking control method are studied. This article uses a data-driven modeling method. Firstly, the actual operation data of high-speed trains based on traction and braking forces are divided into two parts: traction and braking. Then, the linear models of the traction and braking models of the train, which can be driven very fast, are established respectively. These two models adopt the recursive least square method. Then use implicit generalized predictive control to achieve tracking control of high-speed train speed. The simulation results based on the operating process data of CRH380AL is given that this method can make the requirements that belong to high-speed train operation.