Explicit Solution for Improvement of Real-time Performance of Model Predictive Control in Intelligent Vehicle Stability

doi:10.3901/JME.2025.10.263

Abstract

Abstract: Model predictive control(MPC) has many advantages. However, the complexity of the optimization algorithm arises due to the multi-constraints and nonlinear character of its dynamic model when applied to yaw stability control in intelligent vehicles, making it challenging to achieve sufficiently short control cycle time and step. Therefore, an online explicit solution method for intelligent vehicle yaw stability control based MPC is proposed. It uses Taylor expansion to convert nonlinear model predictive control(NMPC) to linear time-varying model predictive control(LTV-MPC), and then designs a rolling adjustment weight coefficient to convert inequality constrained optimization to unconstrained optimization that can be directly and explicitly solved, so as to avoid multi-step iterative optimization and speed up computation time of MPC. The simulation results indicate that the proposed explicit solution can increase the solving speed of MPC by 3-4 times while ensuring the same control effectiveness, which can significantly improve the real-time performance of MPC in vehicle yaw stability.

Key words: vehicle yaw stability, model predictive control, real-time, penalty function, explicit solution

CLC Number:

TP273

JIAO Hengchao, SHU Ran, ZENG Zhaofeng, TANG Xiaolin, SHU Hongyu. Explicit Solution for Improvement of Real-time Performance of Model Predictive Control in Intelligent Vehicle Stability[J]. Journal of Mechanical Engineering, 2025, 61(10): 263-275.

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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.2025.10.263

http://www.cjmenet.com.cn/EN/Y2025/V61/I10/263

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