智能汽车稳定性MPC实时性改进的显式解方法

doi:10.3901/JME.2025.10.263

摘要/Abstract

摘要： 模型预测控制(Model predictive control,MPC)具有很多优点，但用于智能汽车横摆稳定性控制时，由于其动力学模型的多约束和非线性问题，致使MPC优化算法复杂，难以实现足够短的控制周期和步长。为此，提出一种智能汽车横摆稳定性MPC的在线显式求解方法，使用泰勒展开将非线性模型预测控制(Nonlinear model predictive control,NMPC)转换为线性时变模型预测控制(Linear time-varying model predictive control,LTV-MPC)。再使用滚动调整的权重系数，将不等式约束优化转换为能直接显式求解的无约束优化，以避免多步迭代寻优、加快MPC求解速度。仿真试验结果表明，在保证相同控制效果前提下，所提出的显式解方法能使MPC的求解速度提高3～4倍，可显著提高智能汽车横摆稳定性MPC的实时性。

关键词: 车辆横摆稳定性, 模型预测控制, 实时性, 惩罚函数, 显式求解

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

中图分类号:

TP273

焦恒超, 舒然, 曾兆枫, 唐小林, 舒红宇. 智能汽车稳定性MPC实时性改进的显式解方法[J]. 机械工程学报, 2025, 61(10): 263-275.

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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