Explicit Model Predictive Control for Intelligent Vehicle Lateral Trajectory Tracking

doi:10.16182/j.issn1004731x.joss.19-0684

Abstract

Abstract: To ensure the accuracy, driving stability and online real-time of intelligent vehicle tracking control, an explicit model predictive tracking control method is designed. The cost functions and constraints for tracking accuracy and driving stability in the prediction time domain are proposed. The tracking control problem is transformed into the optimization of the active steering angle with dynamic disturbances. To improve the real-time performance, the traditional model predictive control system is transformed into an equivalent explicit polyhedral piece-wise affine (PPWA) system, and the active steering angle of front wheel is gained by the explicit law on parameter partition. Carsim and Simulink simulation results show that the mean lateral position error is 0.1956m and the mean heading angle error is 0.276° with this method, meanwhile the maximum lateral load-conversion is improved by a rate of 5.92% and the maximum tire utilization adhesion coefficient is improved by a rate of 9.81%, and the average single-step running speed is improved by a rate of 53.97%.

Key words: intelligent vehicle, trajectory tracking control, lateral stability, Explicit Model Predict Control (EMPC)

CLC Number:

Leng Yao, Zhao Shuen. Explicit Model Predictive Control for Intelligent Vehicle Lateral Trajectory Tracking[J]. Journal of System Simulation, 2021, 33(5): 1177-1187.

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