High-speed train trajectory tracking based on event-triggered predictive control
The open operating environment of high-speed trains exposes them to disturbances from external resistances,which are difficult to characterize through modeling.To address this issue,this paper investigates a train model predictive tracking control(TMPC)strategy,with safety,ride comfort and punctuality as the tracking objectives.Firstly,a multi-objective predictive control model with con-straints was constructed for high-speed trains,and the Tube invariant set of the system was calculated to tightly constrain train states and control forces.On this basis,in order to reduce the computational workload for solving the optimal control online,a dynamic event-trig-gered Tube model predictive control(DETMPC)strategy was designed,enabling the optimal control sequence to be solved only when the triggering conditions were satisfied.Furthermore,simulation verification was carried out using Shanghai urban trains running on real tracks.The results show that the control strategy proposed in this paper not only guarantee good tracking performance,but also reduce the frequencies of triggering the model predictive control(MPC),lowering the workload of online calculations significantly.
high-speed trainautomatic train operationtrajectory trackingmodel predictive controlevent-triggered control
万方数据
维普
