Robust nonlinear model predictive control for automatic train operation based on constraint tightening strategy
Chao Long Jia, Hongze Xu, Longsheng Wang
Abstract
Abstract This paper studies the problem of automatic train operation (ATO) robust nonlinear model predictive control under considering multiple objectives and constraints. After establishing a nonlinear multipoint model with uncertain bounded disturbance, a robust nonlinear model predictive control algorithm to meet the punctuality of train operation and energy consumption for ATO is proposed based on constraint tightening strategy. Moreover, theoretical analysis of the feasibility and stability for the speed loop system are presented. Then, with the objective of reference electromagnetic torque tracking and low switching frequency, a model predictive direct torque control algorithm with one‐step delay compensation is proposed. Feasibility of the proposed algorithm is ensured by using deadlock prediction method, and convergence analysis of the torque loop is given simultaneously. Lastly, the effectiveness of these two algorithms are verified by numerical simulation.