Efficient Nonlinear Model Predictive Control of Automated Vehicles

Shuyou Yu (), Encong Sheng, Yajing Zhang, Yongfu Li, Hong Chen and Yi Hao
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Shuyou Yu: State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
Encong Sheng: Department of Control Science and Engineering, Jilin University, Changchun 130022, China
Yajing Zhang: Department of Control Science and Engineering, Jilin University, Changchun 130022, China
Yongfu Li: Department of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Hong Chen: Department of Control Science and Engineering, Jilin University, Changchun 130022, China
Yi Hao: Dongfeng Commercial Vehicle Technology Center, Dongfeng Motor Corporation, Wuhan 442001, China

Mathematics, 2022, vol. 10, issue 21, 1-23

Abstract: In this paper, an efficient model predictive control (MPC) of velocity tracking of automated vehicles is proposed, in which a reference signal is given a priori . Five degree-of-freedom vehicle dynamics with nonlinear tires is chosen as the prediction model, in which coupling characteristics of longitudinal and lateral dynamics are taken into account. In order to balance computational burden and prediction accuracy, Koopman operator theory is adopted to transform the nonlinear model into a global linear model. Then, the global linear model is used in the design of MPC to reduce online computational burden and avoid solving nonconvex/nonlinear optimization problems. Furthermore, the effectiveness of Koopman operator in vehicle dynamics control is verified using a Matlab/Simulink environment. Validation results demonstrate that dynamic mode decomposition with control (DMDc) and extended dynamic mode decomposition (EDMD) algorithms are more accurate in model validation and dynamic prediction than local linearization, and DMDc algorithm has less computational burden on solving optimization problems than the EDMD algorithm.

Keywords: automated vehicle control; nonlinear model predictive control; Koopman operator; data-driven control (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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