East China Jiaotong University

Crsc Research & Design Institute Group Co., Ltd.

Yating Fu

Hui Yang

Zhongqi Li

Ling Liu

Junfeng Cui

Chang Tan

Yu Wang

Yinghe Tong

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

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

Yating Fu