Zheng Xiaoyuan, Zhang Hao, Yan Huaicheng, Yang Fuwen, Wang Zhuping, Vlacic Ljubo
IEEE Trans Cybern. 2020 Jul;50(7):3113-3124. doi: 10.1109/TCYB.2019.2891960. Epub 2019 Feb 11.
This paper is concerned with the adaptive backstepping control problem for a cloud-aided nonlinear active full-vehicle suspension system. A novel model for a nonlinear active suspension system is established, in which uncertain parameters, unknown friction forces, nonlinear springs and dampers, and performance requirements are considered simultaneously. In order to deal with the nonlinear characteristics, a backstepping control strategy is developed. Meanwhile, an adaptive control strategy is proposed to handle the uncertain parameters and unknown friction forces. In the cloud-aided vehicle suspension system framework, the adaptive backstepping controller is updated in a remote cloud based on the cloud storing information, such as road information, vehicle suspension information, and reference trajectories. Finally, simulation results for a full vehicle with 7-degree of freedom model are provided to demonstrate the effectiveness of the proposed control scheme, and it is shown that the addressed controller can improve the performances more than 80% compared with passive vehicle suspension systems.
本文研究了云辅助非线性主动全车辆悬架系统的自适应反步控制问题。建立了一种新型的非线性主动悬架系统模型，该模型同时考虑了不确定参数、未知摩擦力、非线性弹簧和阻尼器以及性能要求。为了处理非线性特性，开发了一种反步控制策略。同时，提出了一种自适应控制策略来处理不确定参数和未知摩擦力。在云辅助车辆悬架系统框架中，自适应反步控制器基于云存储的信息（如道路信息、车辆悬架信息和参考轨迹）在远程云中进行更新。最后，给出了七自由度整车模型的仿真结果，以证明所提控制方案的有效性，结果表明，与被动车辆悬架系统相比，所设计的控制器可将性能提高80%以上。
