熊璐 , 杨兴 , 卓桂荣 , 等 . 无人驾驶车辆的运动控制发展现状综述 [J ] . 机械工程学报 , 2020 , 56 ( 10 ): 127 - 143 . DOI: 10.3901/JME.2020.10.127 http://doi.org/10.3901/JME.2020.10.127 回顾无人驾驶车辆的运动控制问题。从系统模型、控制方法以及控制结构等角度切入，分别在纵向运动控制、路径跟踪控制和轨迹跟踪控制三个层面对国内外的研究进展进行综述，并提出对无人驾驶车辆运动控制技术的发展展望。当前运动控制研究多集中于常规工况，为实现无人驾驶车辆在处理人类驾驶员认为具有挑战性或缺乏操纵能力的复杂动态场景下的潜力，运动控制研究须从常规工况向极限工况拓展，但是极限工况下车辆的非线性和多维运动耦合特征显著增强，对系统建模以及算法的自适应性和鲁棒性的要求进一步提高。同时，为应对复杂场景下的多目标协调优化问题，考虑环境不确定性的运动规划与控制集成设计需要深入研究。增加执行器手段可以提升极限工况下车辆的侧向响应速度和控制裕度，但是冗余异构执行器的控制分配研究仍有待突破。运动控制的实现依赖于路面附着系数、质心侧偏角等信息输入，因此基于多源传感信息融合的关键状态与参数估计问题亟需解决。此外，将机器学习应用到车辆运动控制领域也是一个重要的发展方向。

XIONG L , YANG X , ZHUO G R , et al . Review on motion control of autonomous vehicles [J ] . Journal of Mechanical Engineering , 2020 , 56 ( 10 ): 127 - 143 . (in Chinese) DOI: 10.3901/JME.2020.10.127 http://doi.org/10.3901/JME.2020.10.127 The motion control problem of autonomous vehicles is reviewed. From the perspective of model, algorithm, and control structure, the domestic and foreign research progress is reviewed at three levels of longitudinal motion control, path following and trajectory tracking control, and the development prospect of motion control technology for autonomous vehicles is proposed. The current motion control research mainly focuses on normal conditions. In order to realize the potential of autonomous vehicles in handling critical scenarios that human drivers find challenging or lack the ability to navigate, it is necessary to extend the research to extreme working conditions. However, the properties of non-linearity and multi-dimensional coupled dynamics are significantly enhanced in extreme working conditions. The requirements of system modeling and adaptability and robustness of motion control algorithm are further increased. At the same time, in order to deal with the multi-objective coordination in complex scenarios, the integration of motion planning and control considering environmental uncertainty needs to be studied in depth. Adding actuators can increase the lateral response speed and control margin, but the research of control allocation of redundant and heterogeneous actuators is still to be broken through. The realization of motion control depends on road adhesion coefficient, sideslip angle, etc. Therefore, it is urgent to solve the problem of key state and parameter estimation under multi-source sensor information fusion. In addition, the application of machine learning to the field of vehicle motion control is also an important development direction.

ZHA Y F , DENG J X , QIU Y Y , et al . A survey of intelligent driving vehicle trajectory tracking based on vehicle dynamics [J ] . SAE International Journal of Vehicle Dynamics,Stability,and NVH , 2023 , 7 ( 2 ):10-07-02-0014.

BETZ J , ZHENG H R , LINIGER A , et al . Autonomous vehicles on the edge:a survey on autonomous vehicle racing [J ] . IEEE Open Journal of Intelligent Transportation Systems , 2022 , 3 : 458 - 488 .

CHU D F , LI H R , ZHAO C Y , et al . Trajectory tracking of autonomous vehicle based on model predictive control with PID feedback [J ] . IEEE Transactions on Intelligent Transportation Systems , 2022 , 24 ( 2 ): 2239 - 2250 .

LI L , LI J , ZHANG S Y . Review article:state-of-the-art trajectory tracking of autonomous vehicles [J ] . Mechanical Sciences , 2021 , 12 ( 1 ): 419 - 432 .

STANO P , MONTANARO U , TAVERNINI D , et al . Model predictive path tracking control for automated road vehicles:a review [J ] . Annual Reviews in Control , 2023 , 55 : 194 - 236 .

GAO H B , KAN Z , LI K Q . Robust lateral trajectory following control of unmanned vehicle based on model predictive control [J ] . IEEE/ASME Transactions on Mechatronics , 2022 , 27 ( 3 ): 1278 - 1287 .

ZHANG K W , SUN Q , SHI Y . Trajectory tracking control of autonomous ground vehicles using adaptive learning MPC [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2021 , 32 ( 12 ): 5554 - 5564 . DOI: 10.1109/TNNLS.2020.3048305 http://doi.org/10.1109/TNNLS.2020.3048305 In this work, an adaptive learning model predictive control (ALMPC) scheme is proposed for the trajectory tracking of perturbed autonomous ground vehicles (AGVs) subject to input constraints. In order to estimate the unknown system parameter, we propose a set-membership-based parameter estimator based on the recursive least-squares (RLS) technique with the ensured nonincreasing estimation error. Then, the estimated system parameter is employed in MPC to improve the prediction accuracy. In the proposed ALMPC scheme, a robustness constraint is introduced into the MPC optimization to handle parametric and additive uncertainties. For the designed robustness constraint, its shape is decided off-line based on the invariant set, whereas its shrinkage rate is updated online according to the estimated upper bound of the estimation error, leading to further reduced conservatism and slightly increased computational complexity compared with the robust MPC methods. Furthermore, it is theoretically shown that the proposed ALMPC algorithm is recursively feasible under some derived conditions, and the closed-loop system is input-to-state stable (ISS). Finally, a numerical example and comparison study are conducted to illustrate the efficacy of the proposed method.

许芳 , 张君明 , 胡云峰 , 等 . 智能车辆路径跟踪横纵向耦合实时预测控制器 [J ] . 吉林大学学报(工学版) , 2021 , 51 ( 6 ): 2287 - 2294 .

XU F , ZHANG J M , HU Y F , et al . Lateral and longitudinal coupling real-time predictive controller for intelligent vehicle path tracking [J ] . Journal of Jilin University (Engineering and Technology Edition) , 2021 , 51 ( 6 ): 2287 - 2294 . (in Chinese)

唐泽月 , 刘海鸥 , 薛明轩 , 等 . 基于MPC-MFAC的双侧独立电驱动无人履带车辆轨迹跟踪控制 [J ] . 兵工学报 , 2023 , 44 ( 1 ): 129 - 139 . DOI: 10.12382/bgxb.2022.0886 http://doi.org/10.12382/bgxb.2022.0886 简化模型带来的模型失配以及外部环境不确定性是导致轨迹跟踪误差的主要原因，尤其对于无人履带车辆，其复杂的物理特性和工作环境更放大了两大因素的不利影响。针对该问题，将基于模型和基于数据的控制方法结合起来，提出一种基于模型预测控制结合无模型自适应控制补偿的双侧独立电驱动无人履带车辆轨迹跟踪控制方法。在平衡建模准确度和求解耗时的基础上，利用模型预测控制进行前馈求解。针对模型预测控制中简化模型与车辆实际模型之间必然存在的差异以及环境不确定性，基于动态跟踪效果构建无模型自适应控制算法进行补偿，即利用车辆实际轨迹与模型预测所得轨迹之间的误差，对模型预测控制求解的两侧履带速度控制量进行实时修正。仿真实验结果表明，该方法能够在一定程度上抑制系统内外部不确定因素的影响，提高动态环境下双侧独立电驱动无人履带车辆轨迹跟踪控制的精度。

TANG Z Y , LIU H O , XUE M X , et al . Trajectory tracking control of dual independent electric drive unmanned tracked vehicle based on MPC-MFAC [J ] . Acta Armamentarii , 2023 , 44 ( 1 ): 129 - 139 . (in Chinese) DOI: 10.12382/bgxb.2022.0886 http://doi.org/10.12382/bgxb.2022.0886 The model mismatch caused by the simplified model and uncertainty of external environment are the main reasons for the trajectory tracking error. Especially for the unmanned tracked vehicle, its complex physical characteristics and working environment magnify the adverse effects of these two factors. To solve this problem, this paper combines the model-based and data-based control methods, and proposes a trajectory tracking control method for the dual independent electric drive unmanned tracked vehicle based on a model predictive control algorithm (MPC) combined with a model-free adaptive control algorithm (MFAC) as compensation. Firstly, based on balancing modeling accuracy and solution time, the MPC is used for feedforward solution. Then, for the inevitable differences between the simplified model in the MPC and the actual vehicle model and environmental uncertainty, the MFAC algorithm is constructed based on the dynamic tracking effect for compensation. That is, the error between the actual trajectory of the vehicle and the trajectory predicted by the model is used to correct the speed control quantities of the dual tracks solved by the MPC in real time. The simulation results show that this method can suppress the influence of internal and external uncertainties of the system to a certain extent, and improve the trajectory tracking control accuracy of the dual independent electric drive unmanned tracked vehicle in a dynamic environment.

WIDNER A , TIHANYI V , TETTAMANTI T . Framework for vehicle dynamics model validation [J ] . IEEE Access , 2022 , 10 : 35422 - 35436 .

WANG K , YANG M L , LI Y , et al . Multidirectional motion coupling based extreme motion control of distributed drive autonomous vehicle [J ] . Scientific Reports , 2022 , 12 ( 1 ): 13203 . DOI: 10.1038/s41598-022-17351-4 http://doi.org/10.1038/s41598-022-17351-4 To improve the multidirectional motion control accuracy and driving stability of Distributed Drive Autonomous Vehicles (DDAVs) under extreme conditions, the extreme speed estimation method based on dynamic boundary and the multidirectional motion coupling control law design method based on multi degrees of freedom vehicle dynamic model are proposed. The extreme speed estimation method identifies the stable state of DDAVs by the dynamic boundary composed of yaw rate, sideslip angle and roll angle, and then estimates the extreme speed of the vehicle. The design method of multidirectional motion coupling control law adopts eight-degrees-of-freedom (8-DOF) vehicle dynamic model to design path tracking control law, speed tracking control law, yaw stability control law, and active suspension control law at the same time, so as to realize multidirectional motion coupling control. Based on the above method, the Multidirectional Motion Coupling Control System (MMCCS) of DDAVs is designed. The effectiveness of the proposed method is verified by double-line-shifting and serpentine driving simulations under different road adhesion conditions. The superiority of the method is proved by comparing the existing integrated control method.© 2022. The Author(s).

CHEN G Y , ZHAO X M , GAO Z H , et al . Dynamic drifting control for general path tracking of autonomous vehicles [J ] . IEEE Transactions on Intelligent Vehicles , 2023 , 8 ( 3 ): 2527 - 2537 .

FENG S , LI X G , ZHANG S , et al . A review:state estimation based on hybrid models of Kalman filter and neural network [J ] . Systems Science & Control Engineering , 2023 , 11 ( 1 ): 2173682 .

KIM D , MIN K , KIM H , et al . Vehicle sideslip angle estimation using deep ensemble-based adaptive Kalman filter [J ] . Mechanical Systems and Signal Processing , 2020 , 144 : 106862 .

MURTA J B , VICTORINO A C , BAÊTA J G . A model-less approach for estimating vehicles sideslip angle by a neural network concept [C ] // Proceedings of 2021 IEEE International Conference on Mechatronics . Kashiwa,Japan : IEEE , 2021 : 1 - 6 .

VISET F , HELMONS R , KOK M . An extended Kalman filter for magnetic field slam using Gaussian process regression [J ] . Sensors , 2022 , 22 ( 8 ): 2833 .

XIAO F , LI C R , FAN Y X , et al . State of charge estimation for lithium-ion battery based on Gaussian process regression with deep recurrent kernel [J ] . International Journal of Electrical Power & Energy Systems , 2021 , 124 : 106369 .

DANG W , LIAO S J , YANG B , et al . An encoder-decoder fusion battery life prediction method based on Gaussian process regression and improvement [J ] . Journal of Energy Storage , 2023 , 59 : 106469 .

LÜ X , HU B Q , LI K L , et al . An adaptive and robust UKF approach based on Gaussian process regression-aided variational Bayesian [J ] . IEEE Sensors Journal , 2021 , 21 ( 7 ): 9500 - 9514 .

TOLBA H , DKHILI N , NOU J , et al . Multi-horizon forecasting of global horizontal irradiance using online Gaussian process regression:a kernel study [J ] . Energies , 2020 , 13 ( 16 ): 4184 .