East China Jiaotong University

Crsc Research & Design Institute Group Co., Ltd.

Zhongqi Li

Hui Yang

Yating Fu

Ling Liu

Junfeng Cui

Yanli Zhou

Luojun Yang

Feng Xu

Mengying Li

Yu Wang

Liang Zhou

Yinghe Tong

Miao Yan

Chang Tan

The invention discloses a method and a system for state feedback predictive control of a high-speed train based on a forecast error. The method comprises: obtaining a speed prediction model of a high-speed train predicting speeds of the train at times k and k+p according to the speed prediction model; obtaining an actual speed output value of the train; determining a speed prediction error at time k according to the prediction speed of the train and the actual speed output value of the train; correcting a prediction speed of the train at time k+p, according to the speed prediction error, to obtain a corrected prediction speed of the train; calculating a control force uof the train according to u=λ(p)[y−y−Kx+ŷ]; and applying a control force to the train based on the control force u. The disclosure may have features of strong adaptability and easy calculation, and achieve high-precision tracking of train speed.

Method and system for state feedback predictive control of high-speed train based on forecast error

A data-driven integral sliding mode control method, system and device for a high-speed electric multiple unit (EMU) is provided, which relates to the field of operation control of EMUs. The method includes performing kinetic analysis on an operation process of a high-speed EMU to enable an input and output data set of the EMU to be equivalent to a multi-input-multi-output (MIMO) discrete-time nonlinear system; constructing an EMU full format dynamic linearization (FFDL) data model involving a generalized disturbance based on the MIMO discrete-time nonlinear system; designing an equivalent control law and a switching control law based on the FFDL data model; and establishing a MIMO EMU integral sliding mode control law, thus controlling the operation of the high-speed EMU. According to the present disclosure, the integral sliding mode control law is deduced based on the FFDL data model to conduct nonlinear control of the EMU.

Data-driven integral sliding mode control method, system and device for high-speed electric multiple unit

Disclosed are a method and a system for multi-objective optimization of urban train operation. Firstly, speed limit information, slope information and curve radius information of a real train route are obtained, a section is segmented into non-equal sub-sections according to the above information about actual line characteristics, and then a longitudinal dynamics model of the train is constructed in combination with basic vehicle data of the train. Next, energy consumption of the train operation, section operation time, actual parking positions, and rates of acceleration change are calculated, so as to construct a multi-objective optimization model of train operation. Afterwards, a multi-objective differential evolution algorithm is used to solve the multi-objective optimization model, in order to obtain a Pareto optimal solution set of each operation district. Finally, an optimal solution is obtained which takes all objectives into comprehensive consideration, and an optimal train speed curve is generated.

Method and system for multi-objective optimization of urban train operation

The present disclosure provides a method and system for controlling a heavy-haul train based on reinforcement learning. The method includes: obtaining operation state information of a heavy-haul train at a current time point; obtaining a heavy-haul train action of a next time point according to the operation state information of the heavy-haul train at the current time point and a heavy-haul train virtual controller, and sending the heavy-haul train action of the next time point to a heavy-haul train control unit to control operation of the heavy-haul train. The heavy-haul train virtual controller is obtained by training a reinforcement learning network according to operation state data of the heavy-haul train and an expert strategy network; the reinforcement learning network includes one actor network and two critic networks; the reinforcement learning network is constructed according to a soft actor-critic (SAC) reinforcement learning algorithm.

Method and system for controlling heavy-haul train based on reinforcement learning

Growing community of inventors

Nanchang, China

Zhongqi Li