基于模型预测的主动转向智能无人车辆横向稳态控制

A Lateral Stability Control Strategy of Active Steering Intelligent Unmanned Vehicle Based on Model Predictive

在复杂路况以及智能无人车辆转向过程中,车辆处于一种时变非稳定性状态,影响车辆操纵安全性和行驶稳定性.针对此问题,提出了一种应用于前轮主动转向无人车辆的优化模型预测控制策略,该控制器采用分层结构对车辆前轮转角进行优化控制,以改善车身在非稳态下的特征.考虑到无人车辆实际行驶时存在的内部和外部扰动因素,建立了非线性扰动量平台模型和非线性轮胎模型,并通过模块化方法,构建了无人车辆非线性动力学模型,利用非线性扰动观测器对干扰进行抑制,提高了车辆模型精确性与控制器控制效果.经仿真实验对比和硬件在环实验测试,结果表明:与无横向稳态控制车辆相比,稳定性提高了55%,验证了该策略能显著改善车辆的横向稳定性及有效性.

In the complex road conditions and the steering process of intelligent unmanned vehicle,the vehicle is in a time-varying unstable state,causing the vehicle handling insecurity and driving instability.In this paper,an optimal model predictive control strategy was proposed for the front wheel active steering unmanned vehicle.Firstly,a hierarchical structure was designed for the controller to optimize the front wheel steering angle of the vehicle and to improve the characteristics of the vehicle body under the unsteady state.And then,taking into ac�count the internal and external disturbance factors of the unmanned vehicle in actual driving,the nonlinear dis�turbance platform model and nonlinear tire model were established,and the nonlinear dynamic model of the un�manned vehicle was built through the modular method.Finally,a nonlinear disturbance observer was used to suppress the disturbance,improving the accuracy of the vehicle model and the control effect of the controller.The results of simulation and hardware-in-the-loop test show that the stability of the vehicle can be improved by 55%compared with the vehicle without lateral stability control,verifying that the control strategy can signific�antly improve the lateral stability and effectiveness of the vehicle.