Like many complex systems,the electric multiple unit(EMU)operation process also has the characterist-ics of multivariable,strong coupling and nonlinearity,which seriously affect the performance of the train control system.A new multi-input-multi-output(MIMO)data-driven integral sliding mode predictive control(ISMPC)al-gorithm is proposed for the EMU autopilot system with external disturbances.Based on the full format dynamic lin-earization(FFDL)data model equivalent to the EMU operation process,a discrete integral sliding mode control(ISMC)law is designed.To achieve higher output tracking error accuracy,the switching control with ISMC is re-placed by model predictive control(MPC),leading to the further derivation of the ISMPC algorithm.Through the delay estimation of the unknown disturbance,parameter error and other uncertainties of the FFDL data model,the control performance of the algorithm and the equivalent description of the system are improved.After providing the stability proof analysis of the two algorithms,the ISMC and ISMPC algorithms proposed in this paper are simu-lated and tested on the CRH380A EMU simulation test bench equipped in the laboratory,and compared with oth-er methods.The simulation results show that the ISMPC algorithm has better control performance,and the speed tracking error of each power unit of the EMU is within±0.132 km/h,which meets the tracking accuracy require-ments of the train;The control force and acceleration are within[-52 kN,42 kN]and±0.9249 m/s2 respectively and change smoothly.
关键词
列车自动驾驶/数据驱动控制/速度跟踪/积分滑模控制/模型预测控制/全格式数据模型
Key words
Train automatic driving/data-driven control/speed tracking/integral sliding mode control(ISMC)/model predictive control(MPC)/full format data model
维普
万方数据