自动驾驶汽车的无偏移非线性模型预测控制研究

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【摘要】为解决自动驾驶汽车运动控制中基于横向-纵向耦合结构的无偏移非线性模型预测控制（OF-NMPC）的稳态误差问题，使用无迹卡尔曼滤波器观察控制器状态和扰动，并纳入模型预测和参考值计算以消除稳态误差。仿真和实车试验结果表明，所提出的OF-NMPC算法可有效消除稳态误差，提高系统的动态性能。

关键词：运动控制 轮胎模型 非线性模型预测控制 路径跟踪

中图分类号：U461.91   文献标志码：A   DOI： 10.20104/j.cnki.1674-6546.20230153

Offset Free Nonlinear Model Predictive Controller for Autonomous Vehicles

【Abstract】Nonlinear Model Predictive Control （NMPC） method-based motion control has attracted considerable attention in the field of autonomous driving. However， the steady-state error problem has not been comprehensively investigated， especially for nonlinear MPC. This paper seeks to solve the steady-state error problem based on Offset-Free NMPC （OF-NMPC） with a lateral-longitudinal coupling structure. The proposed OF-NMPC uses an Unscented Kalman Filter （UKF） to observe the states and disturbances and incorporates them into the prediction model and reference calculation to eliminate the steady-state error. One of the challenges of OF-NMPC is the need to use optimization methods to obtain reference values， which will obviously increase the considerable computational burden. Based on the appropriate simplification， we get the reference analytical solution without solving nonlinear optimization problems online in real-time. Simulation and real vehicle experiments show that the proposed OF-NMPC can effectively eliminate the steady-state error and improve the system’s dynamic performance.

Key words： Motion control， Tire model， Nonlinear Model Predictive Control （NMPC）， Path tracking

1 前言

基于模型预测控制（Model Predictive Control，MPC）的自动驾驶跟随控制技术和稳定性控制技术近年得到了广泛研究。（剩余6427字）