Design and experiential test of a model predictive path following control with adaptive preview for autonomous buses
Design and experiential test of a model predictive path following control with adaptive preview for autonomous buses
复制标题
自动驾驶公交车自适应预览模型预测路径跟随控制的设计与体验测试
DOI：
10.1016/j.ymssp.2021.107701
复制
发表时间：
2021-02-26
影响因子：
8.4
通讯作者：
Han, Mo
中科院分区：
文献类型：
--
作者：
He, Hongwen;Shi, Man;Han, Mo
本文提出了一种两轴自主公交车的分层路径跟踪控制框架，在该框架下，主动安全控制器、自适应预见调节器以及预测轨迹跟踪器集成为一个具有高效性能的整体系统。该框架分为两层。上层设计通过主动安全控制器限制车速，防止车辆侧滑和侧翻。由于优化算法的高计算量对车辆系统的实时应用提出了很大的挑战，因此，自适应预见调节器的PSO和SVM的积极结合，旨在更实际的实施。回到下层，提出了预测轨迹控制器和PI控制器来获得满足稳定性约束的转向角，以跟踪参考路径，并通过仿真和实验验证了所提出方法的有效性。基于TruckMaker-Simulink联合平台，采用高保真整车模型，在不同场景下进行了4种工况的联合对比仿真。与无预见和有固定预见的情况相比，该控制的跟踪精度分别提高了44.97%和36.12%。所提出的方法进行了测试，在一个真实的自主总线与仿真相比，可接受的误差。此外，在真实的自主公交车上验证了所提出的分层控制框架的有效性及其实时控制能力，在参数不确定性和外部干扰下具有令人满意的性能。本文针对双轴自主公交车提出了一种分层路径跟踪控制框架，自适应预见调节器以及预测轨迹跟踪器被集成为具有高效性能的整体系统。该框架分为两层。上层设计通过主动安全控制器限制车速，防止车辆侧滑和侧翻。由于优化算法的高计算量对车辆系统的实时应用提出了很大的挑战，因此，自适应预见调节器的PSO和SVM的积极结合，旨在更实际的实施。回到下层，提出了预测轨迹控制器和PI控制器来获得满足稳定性约束的转向角，以跟踪参考路径，并通过仿真和实验验证了所提出方法的有效性。基于TruckMaker-Simulink联合平台，采用高保真整车模型，在不同场景下进行了4种工况的联合对比仿真。与无预见和有固定预见的情况相比，该控制的跟踪精度分别提高了44.97%和36.12%。所提出的方法进行了测试，在一个真实的自主总线与仿真相比，可接受的误差。此外，在真实的自主公交系统中验证了所提出的分层控制框架的有效性和实时控制能力，在参数不确定性和外部干扰下具有良好的控制性能。(c)2021由Elsevier Ltd.出版
This paper presents a hierarchical path following control framework for a two-axle autonomous bus, under which the active safety controller, the adaptive preview regulator as well as the predictive trajectory tracker are integrated as a whole system with highly effective performance. The framework is developed with two layers. The upper layer is designed to prevent the vehicle from sideslip and rollover by restricting the speed with an active safety controller. As the high computational load of optimal algorithm poses a great challenge for the real-time application in vehicle system, an adaptive preview regulator is, therefore, developed with an active combination of the PSO and SVM aiming for a more practical implementation. Back to the lower layer, the predictive trajectory controller and PI controller are proposed to acquire the steering angle with stability constrains to follow the reference path. The whole system with the proposed method is verified with both the simulation and experiments. A combined comparison simulation with four cases was carried out based on TruckMaker-Simulink joint platform using a highly-fidelity and full-car model in different scenarios. Compared with the case without preview and that with fixed preview, the tracking accuracy of the proposed control is improved by 44.97% and 36.12% respectively. The proposed method is tested in a real autonomous bus with acceptable errors compared with the simulation. In addition, the effectiveness of the proposed layered control framework and its real-time control capability is proved in real autonomous bus with satisfying performance under parameter uncertainties and external disturbances.This paper presents a hierarchical path following control framework for a two-axle autonomous bus, under which the active safety controller, the adaptive preview regulator as well as the predictive trajectory tracker are integrated as a whole system with highly effective performance. The framework is developed with two layers. The upper layer is designed to prevent the vehicle from sideslip and rollover by restricting the speed with an active safety controller. As the high computational load of optimal algorithm poses a great challenge for the real-time application in vehicle system, an adaptive preview regulator is, therefore, developed with an active combination of the PSO and SVM aiming for a more practical implementation. Back to the lower layer, the predictive trajectory controller and PI controller are proposed to acquire the steering angle with stability constrains to follow the reference path. The whole system with the proposed method is verified with both the simulation and experiments. A combined comparison simulation with four cases was carried out based on TruckMaker-Simulink joint platform using a highly-fidelity and full-car model in different scenarios. Compared with the case without preview and that with fixed preview, the tracking accuracy of the proposed control is improved by 44.97% and 36.12% respectively. The proposed method is tested in a real autonomous bus with acceptable errors compared with the simulation. In addition, the effectiveness of the proposed layered control framework and its real-time control capability is proved in real autonomous bus with satisfying performance under parameter uncertainties and external disturbances.(c) 2021 Published by Elsevier Ltd.