参数自优化的有人与无人车辆编队鲁棒模型预测控制

为解决有人与无人车辆编队中，有人领航车紧急加减速和紧急转向控制输入对无人车跟踪控制的扰动问题，设计了一种参数自优化的有人与无人车辆编队鲁棒模型预测控制算法。通过采集分析历史数据确定控制器扰动的噪声极值，并经过适度放缩得到其鲁棒边界。设计抑制该扰动的局部反馈鲁棒控制器，并通过贝叶斯优化的方法实现鲁棒边界等控制器参数自优化。基于混合整数线性优化的方法预测有人领航车未来轨迹，并设计鲁棒模型预测控制器实现无人车对有人领航车的跟踪控制。仿真和实车试验结果表明：所设计的鲁棒模型预测控制器在跟踪精度方面相比于传统模型预测控制器有明显的提升；同时该控制器有效地抵抗了来自有人领航车紧急加减速和紧急转向控制输入、无人跟随车系统模型不确定性和外部环境的扰动，振荡情况明显改善，提高了系统的鲁棒性。

Robust Model Predictive Control for Manned and Unmanned Vehicle Formation Based on Parameter Self-Optimization

To solve the problem of disturbances in unmanned vehicle tracking control caused by the emergency acceleration, deceleration and steering control input of the manned leading vehicle in a formation of manned and unmanned vehicles, a parameter self-optimizing robust model predictive controller is designed. The noise extremum of the disturbances is determined by collecting and analyzing the historical data, which is scaled moderately to obtain a robust boundary. A local feedback robust controller is designed to restrain the disturbances, and the controller’s parameters are automatically optimized using the Bayesian optimization algorithm. The mixed-integer linear optimization method is used to predict the trajectory of the leading vehicle, and a robust model predictive controller is proposed to track the leading vehicle using an unmanned vehicle. The simulation and experimental results show that the robust model predictive controller designed in this paper has a significant improvement in tracking accuracy compared with traditional controllers. The controller also effectively restrains the disturbances caused by emergency acceleration, deceleration and steering control input of the manned leading vehicle, model uncertainty of unmanned tracking vehicle and other external factors. Vibration is obviously suppressed, and the robustness of the system is enhanced.