Liu Wenjun, Chen Guang, Knoll Alois
Department of Informatics, Technical University of Munich, Munich, Germany.
School of Automotive Studies, Tongji University, Shanghai, China.
Front Neurorobot. 2021 Jan 6;14:617293. doi: 10.3389/fnbot.2020.617293. eCollection 2020.
In this paper, we design a robust model predictive control (MPC) controller for vehicle subjected to bounded model uncertainties, norm-bounded external disturbances and bounded time-varying delay. A Lyapunov-Razumikhin function (LRF) is adopted to ensure that the vehicle system state enters in a robust positively invariant (RPI) set under the control law. A quadratic cost function is selected as the stage cost function, which yields the upper bound of the infinite horizon cost function. A Lyapunov-Krasovskii function (LKF) candidate related to time-varying delay is designed to obtain the upper bound of the infinite horizon cost function and minimize it at each step by using matrix inequalities technology. Then the robust MPC state feedback control law is obtained at each step. Simulation results show that the proposed vehicle dynamic controller can steer vehicle states into a very small region near the reference tracking signal even in the presence of external disturbances, model uncertainties and time-varying delay. The source code can be downloaded on https://github.com/wenjunliu999.
在本文中，我们针对受有界模型不确定性、范数有界外部干扰和有界时变延迟影响的车辆设计了一种鲁棒模型预测控制（MPC）控制器。采用李雅普诺夫 - 拉祖米欣函数（LRF）来确保车辆系统状态在控制律作用下进入一个鲁棒正不变（RPI）集。选择一个二次成本函数作为阶段成本函数，它给出了无限时域成本函数的上界。设计一个与时变延迟相关的李雅普诺夫 - 克拉索夫斯基函数（LKF）候选函数，以获得无限时域成本函数的上界，并通过矩阵不等式技术在每一步将其最小化。然后在每一步获得鲁棒MPC状态反馈控制律。仿真结果表明，即使存在外部干扰、模型不确定性和时变延迟，所提出的车辆动态控制器也能将车辆状态引导至参考跟踪信号附近的一个非常小的区域内。源代码可在https://github.com/wenjunliu999上下载。
