高速动车组数据驱动无模型自适应积分滑模预测控制  

Data-driven Model-free Adaptive Integral Sliding Mode Predictive Control for High-speed Electric Multiple Unit

作　　者：李中奇 周靓 杨辉 LI Zhong-Qi;ZHOU Liang;YANG Hui(School of Electrical and Automation Engineering,East China Jiaotong University,Nanchang 330013;State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure,Nanchang 330013)

机构地区：[1]华东交通大学电气与自动化工程学院,南昌330013 [2]轨道交通基础设施性能监测与保障国家重点实验室,南昌330013

出　　处：《自动化学报》2024年第1期194-210,共17页Acta Automatica Sinica

基　　金：国家自然科学基金(61991404,52162048,62003138);江西省主要学科学术和技术带头人培养项目(20213BCJ22002);流程工业综合自动化国家重点实验室开放基金(2022-KF-21-03)资助。

摘　　要：同许多复杂系统一样,动车组(Electric multiple unit,EMU)运行过程也具有多变量、强耦合以及非线性等特性,这严重影响着列控系统的性能.针对包含外部扰动的动车组自动驾驶系统,提出一种新型的多输入多输出(Multi-input-multi-output,MIMO)数据驱动积分滑模预测控制(Integral sliding mode predictive control,ISMPC)算法.首先,该算法基于与动车组运行过程等效的全格式动态线性化(Full format dynamic linearization,FFDL)数据模型,设计一种离散积分滑模控制(Integral sliding mode control,ISMC)律.为了使系统能够获得更高的输出跟踪误差精度,利用模型预测控制(Model predictive control,MPC)代替ISMC的切换控制,进一步推导出ISMPC算法.同时,通过对FFDL数据模型的未知扰动、参数误差等不确定项进行延时估计,提升了算法的控制性能和对系统的等价描述程度.在提供两种算法的稳定性证明分析之后,以实验室配备的CRH380A型动车组仿真实验台对提出的ISMC和ISMPC算法进行仿真测试,并与其他方法进行对比,仿真结果表明ISMPC算法控制性能较好,动车组各动力单元速度跟踪误差均在±0.132 km/h以内,满足列车的跟踪精度需求;控制力和加速度分别在[-52 kN,42 kN]和±0.9249 m/s2以内且变化平稳.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 

分 类 号：U266[机械工程—车辆工程] U284.48[交通运输工程—载运工具运用工程] TP273[交通运输工程—道路与铁道工程]