卢佳兴 , 刘海鸥 , 关海杰 , 等 . 基于双参数自适应优化的无人履带车辆轨迹跟踪控制 [J ] . 兵工学报 , 2023 , 44 ( 4 ): 960 - 971 .

LU J X , LIU H O , GUAN H J , et al . Trajectory tracking control of unmanned tracked vehicles based on adaptive dual-parameter optimization [J ] . Acta Armamentarii , 2023 , 44 ( 4 ): 960 - 971 . (in Chinese) DOI: 10.12382/bgxb.2022.0009 http://doi.org/10.12382/bgxb.2022.0009 To improve the poor adaptability of trajectory tracking controllers with fixed parameters, an optimized adaptive dual-parameter trajectory tracking algorithm for unmanned tracked vehicles based on the improved Particle Swarm Optimization (IPSO) and Multi-Layer Perceptron (MLP) algorithms is proposed. In the offline state, based on the collected actual vehicle data, the IPSO algorithm is used to construct the optimal parameter data set under different motion primitives, aiming for high accuracy, high stability, and low time cost of trajectory tracking. With the motion primitive type and vehicle speed as feature vectors, control time domain length and control time step length as labels, adaptive learning rate optimization algorithm is used to complete the training of the MLP neural network model. In the online state, according to the trajectory information and vehicle state feedback information provided by the planning layer, the MLP neural network outputs the predicted optimal control time domain length and control time step. These parameters are then input to the model predictive controller as dual parameters, enabling the adaptive trajectory tracking control. ROS-VREP co-simulation test and actual vehicle test based on a bilateral electric drive platform are carried out. Vehicle test results show that under various working conditions including large curvature steering, the proposed controller achieves a 30.5% reduction in average lateral error, a 17.2% decrease in average heading error, and a 7.8% reduction in average change rate of rotation angle, compared with the fixed-parameter trajectory tracking control method with the same calculation time cost. The results verify the feasibility and effectiveness of the new algorithm.

ATTIA R , ORJUELA R , BASSET M . Combined longitudinal and lateral control for automated vehicle guidance [J ] . Vehicle System Dynamics , 2014 , 52 ( 2 ): 261 - 279 . DOI: 10.1080/00423114.2013.874563 http://doi.org/10.1080/00423114.2013.874563 http://www.tandfonline.com/doi/abs/10.1080/00423114.2013.874563 http://www.tandfonline.com/doi/abs/10.1080/00423114.2013.874563

YU Y H , LI Y N , LIANG Y X , et al . Decoupling motion tracking control for 4wd autonomous vehicles based on the path correction [J ] . Proceedings of the Institution of Mechanical Engineers , 2022 , 236 ( 1 ): 99 - 108 .

WEI Z , WU W H , PENG G , et al . Application of linear active disturbance rejection decoupling control for AUV three-dimensional trajectory tracking control [C ] // Proceedings of the 2021 33rd Chinese Control and Decision Conference. Kunming, China:IEEE , 2021 : 5214 - 5219 .

WANG H R , WANG Q D , CHEN W W , et al . A novel path tracking approach considering safety of the intended functionality for autonomous vehicles [J ] . Proceedings of the Institution of Mechanical Engineers , 2022 , 236 ( 4 ): 738 - 752 .

陈特 , 徐兴 , 蔡英凤 , 等 . 基于状态估计的无人车前轮转角与横摆稳定协调控制 [J ] . 北京理工大学学报 , 2021 , 41 ( 10 ): 1050 - 1057 .

CHEN T , XU X , CAI Y F , et al . Coordinated control of front-wheel steering angle and yaw stability for unmanned ground vehicle based on state estimation [J ] . Transactions of Beijing Institute of Technology , 2021 , 41 ( 10 ): 1050 - 1057 . (in Chinese)

LI D P , LI D J . Adaptive neural tracking control for an uncertain state constrained robotic manipulator with unknown time-varying delays [J ] . IEEE Transactions on Systems, Man, and Cybernetics:Systems , 2018 , 48 ( 12 ): 2219 - 2228 . DOI: 10.1109/TSMC.2017.2703921 http://doi.org/10.1109/TSMC.2017.2703921 https://ieeexplore.ieee.org/document/7944578/ https://ieeexplore.ieee.org/document/7944578/

李睿 , 项昌乐 , 王超 , 等 . 自动驾驶履带车辆鲁棒自适应轨迹跟踪控制方法 [J ] . 兵工学报 , 2021 , 42 ( 6 ): 1128 - 1137 . DOI: 10.3969/j.issn.1000-1093.2021.06.002 http://doi.org/10.3969/j.issn.1000-1093.2021.06.002 针对野外环境中自动驾驶履带车辆轨迹跟踪控制问题，考虑建模误差、参数不确定性及外界随机强干扰，以强鲁棒性及精确跟踪为目标，提出一种基于误差符号鲁棒积分的自动驾驶履带车辆鲁棒自适应轨迹跟踪控制方法。基于拉格朗日动力学方程建立自动驾驶履带车辆的运动学与动力学耦合模型；采用自适应控制方法实现对模型的精确前馈补偿，抵消模型非线性的影响；通过误差符号鲁棒积分有效抑制外界干扰及不确定性的影响；利用Lyapunov稳定性理论证明了闭环系统的全局渐进稳定性与收敛性。对仿真结果进行了实车实验一致性验证。仿真和实验结果证明：该方法在存在建模误差、参数不确定性、外界干扰条件下，在实现自动驾驶履带车辆高精度轨迹跟踪控制的同时，具有较强的自适应和鲁棒性。

LI R , XIANG C L , WANG C , et al . Robust adaptive trajectory tracking control approach for autonomous tracked vehicles [J ] . Acta Armamentarii , 2021 , 42 ( 6 ): 1128 - 1137 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2021.06.002 http://doi.org/10.3969/j.issn.1000-1093.2021.06.002 A robust adaptive trajectory tracking control approach based on robust integral of sign of error is presented for the trajectory tracking control of autonomous tracked vehicles in the field environment. In the proposed approach, the modelling errors, parametric uncertainties, and external random and strong disturbances are taken into account. A kinematic and dynamic coupling model of autonomous tracked vehicles is established based on Lagrangian dynamical equation. The feedforward compensation of the established model is realized by adaptive control approach, and the external disturbances and uncertainties can be suppressed by using the robust integral of sign of error. And then the asymptotical global stability and convergence of the closed loop system is demonstrated by Lyapunov stability theory. The simulated results were verified through real vehicle test. Simulated and experimental results show that the proposed approach can be used to realize the high accuracy trajectory tracking and insure the adaptiveness and robustness for autonomous tracked vehicles in the presence of modelling errors, parametric uncertainties and external disturbances.

MA R H , WANG L F , ZHANG J Z . Observer-based prescribed performance adaptive terminal sliding mode control for path tracking of autonomous ground vehicles [C ] // Proceedings of 2020 Chinese Automation Congress. Shanghai, China:IEEE , 2020 : 795 - 801 .

HU C , GAO H B , GUO J H , et al . RISE-based integrated motion control of autonomous ground vehicles with asymptotic prescribed performance [J ] . IEEE Transactions on Systems, Man, and Cybernetics: Systems , 2021 , 51 ( 9 ): 5336 - 5348 . DOI: 10.1109/TSMC.2019.2950468 http://doi.org/10.1109/TSMC.2019.2950468 https://ieeexplore.ieee.org/document/8897128/ https://ieeexplore.ieee.org/document/8897128/

高振宇 , 孙振超 , 郭戈 . 网联车辆有限时间滑模预设性能队列控制 [J ] . 控制理论与应用 , 2023 , 40 ( 1 ): 1 - 11 .

GAO Z Y , SUN Z C , GUO G . Finite-time sliding mode prescribed performance platoon control of connected vehicles [J ] . Control Theory & Applications , 2023 , 40 ( 1 ): 1 - 11 . (in Chinese)

FLESCH C , NORMEY-RICO J E , FLESCH C A . A unified anti-windup strategy for SISO discrete dead-time compensators [J ] . Control Engineering Practice , 2017 , 69 ( 3 ): 50 - 60 . DOI: 10.1016/j.conengprac.2017.09.002 http://doi.org/10.1016/j.conengprac.2017.09.002 https://linkinghub.elsevier.com/retrieve/pii/S0967066117301995 https://linkinghub.elsevier.com/retrieve/pii/S0967066117301995

何友国 , 田肖肖 , 袁朝春 . 考虑输入饱和的车辆队列协同巡航控制算法 [J ] . 重庆理工大学学报(自然科学) , 2020 , 34 ( 9 ): 47 - 55 .

HE Y G , TIAN X X , YUAN C C . Cooperative adaptive cruise control algorithm of vehicular platoon considering input saturation [J ] . Journal of Chongqing University of Technology (Natural Science) , 2020 , 34 ( 9 ): 47 - 55 . (in Chinese)

YANG H J , WANG J , LI H B , et al . Adaptive cooperative control for air-ground systems with actuator saturation under disturbances [J ] . International Journal of Adaptive Control and Signal Processing , 2022 , 37 ( 4 ): 880 - 914 . DOI: 10.1002/acs.v37.4 http://doi.org/10.1002/acs.v37.4 https://onlinelibrary.wiley.com/toc/10991115/37/4 https://onlinelibrary.wiley.com/toc/10991115/37/4

LIANG Z C , SHEN M Y , ZHAO J , et al . Adaptive sliding mode fault tolerant control for autonomous vehicle with unknown actuator parameters and saturated tire force based on the center of percussion [J ] . IEEE Transactions on Intelligent Transportation Systems , 2023 , 24 ( 11 ): 11595 - 11606 . DOI: 10.1109/TITS.2023.3289439 http://doi.org/10.1109/TITS.2023.3289439 https://ieeexplore.ieee.org/document/10173711/ https://ieeexplore.ieee.org/document/10173711/

BU X W , JIANG B X , LEI H M . Low-complexity fuzzy neural control of constrained waverider vehicles via fragility-free prescribed performance approach [J ] . IEEE Transactions on Fuzzy Systems , 2023 , 31 ( 7 ): 2127 - 2139 . DOI: 10.1109/TFUZZ.2022.3217378 http://doi.org/10.1109/TFUZZ.2022.3217378 https://ieeexplore.ieee.org/document/9930639/ https://ieeexplore.ieee.org/document/9930639/

BU X W , JIANG B X , FENG Y N . Hypersonic tracking control under actuator saturations via readjusting prescribed performance functions [J ] . ISA Transactions , 2023 , 134 : 74 - 85 . DOI: 10.1016/j.isatra.2022.08.016 http://doi.org/10.1016/j.isatra.2022.08.016 https://linkinghub.elsevier.com/retrieve/pii/S0019057822004190 https://linkinghub.elsevier.com/retrieve/pii/S0019057822004190

YANG C G , HUANG D Y , HE W , et al . Neural control of robot manipulators with trajectory tracking constraints and input saturation [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2021 , 32 ( 9 ): 4231 - 4242 . DOI: 10.1109/TNNLS.2020.3017202 http://doi.org/10.1109/TNNLS.2020.3017202 https://ieeexplore.ieee.org/document/9179812/ https://ieeexplore.ieee.org/document/9179812/

WANG Y Y , HU J B , LI J , et al . Improved prescribed performance control for nonaffine pure-feedback systems with input saturation [J ] . International Journal of Robust and Nonlinear Control , 2019 , 29 ( 6 ): 1769 - 1788 . DOI: 10.1002/rnc.v29.6 http://doi.org/10.1002/rnc.v29.6 https://onlinelibrary.wiley.com/toc/10991239/29/6 https://onlinelibrary.wiley.com/toc/10991239/29/6

张守武 , 王恒 , 陈鹏 , 等 . 神经网络在无人驾驶车辆运动控制中的应用综述 [J ] . 工程科学学报 , 2022 , 44 ( 2 ): 235 - 243 .

ZHANG S W , WANG H , CHEN P , et al . Overview of the application of neural networks in the motion control of unmanned vehicles [J ] . Chinese Journal of Engineering , 2022 , 44 ( 2 ): 235 - 243 . (in Chinese)

BOUZAIENE R , HAFSI S , BOUANI F . Adaptive neural network PID controller for nonlinear systems [C ] // Proceedings of the 2021 IEEE 2nd International Conference on Signal, Control and Communication.Tunis, Tunisia:IEEE , 2021 : 264 - 269 .

TORK N , AMIRKHANI A , SHOKOUHI S B . An adaptive modified neural lateral-longitudinal control system for path following of autonomous vehicles [J ] . Engineering Science and Technology,an International Journal , 2021 , 24 ( 1 ): 126 - 137 . DOI: 10.1016/j.jestch.2020.12.004 http://doi.org/10.1016/j.jestch.2020.12.004 https://linkinghub.elsevier.com/retrieve/pii/S2215098620342658 https://linkinghub.elsevier.com/retrieve/pii/S2215098620342658

ZHANG Y X , WANG L H , LIU Y D . Adaptive neural network-based path tracking control for autonomous combine harvester with input saturation [J ] . Industrial Robot: the International Journal of Robotics Research and Application , 2021 , 48 ( 4 ): 510 - 522 . DOI: 10.1108/IR-10-2020-0231 http://doi.org/10.1108/IR-10-2020-0231 https://www.emerald.com/insight/content/doi/10.1108/IR-10-2020-0231/full/html https://www.emerald.com/insight/content/doi/10.1108/IR-10-2020-0231/full/html To reduce the effect of parameter uncertainties and input saturation on path tracking control for autonomous combine harvester, a path tracking controller is proposed, which integrates an adaptive neural network estimator and a saturation-aided system.

YUAN X F , HUANG G M , SHI K . Improved adaptive path following control system for autonomous vehicle in different velocities [J ] . IEEE Transactions on Intelligent Transportation Systems , 2019 , 21 ( 8 ): 3247 - 3256 . DOI: 10.1109/TITS.6979 http://doi.org/10.1109/TITS.6979 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

XING B H , XU E Y , WEI J , et al . Recurrent neural network non-singular terminal sliding mode control for path following of autonomous ground vehicles with parametric uncertainties [J ] . IET Intelligent Transport Systems , 2022 , 16 ( 5 ): 616 - 629 . DOI: 10.1049/itr2.v16.5 http://doi.org/10.1049/itr2.v16.5 https://onlinelibrary.wiley.com/toc/17519578/16/5 https://onlinelibrary.wiley.com/toc/17519578/16/5

WANG B Y , LEI Y , FU Y , et al . Autonomous vehicle trajectory tracking lateral control based on the terminal sliding mode control with radial basis function neural network and fuzzy logic algorithm [J ] . Mechanical Sciences , 2022 , 13 ( 2 ): 713 - 724 . DOI: 10.5194/ms-13-713-2022 http://doi.org/10.5194/ms-13-713-2022 https://ms.copernicus.org/articles/13/713/2022/ https://ms.copernicus.org/articles/13/713/2022/ . This paper will study a trajectory tracking control algorithm for electric vehicles based on a terminal sliding mode controller. First, a 3 degrees of freedom nonlinear vehicle model and a controller-oriented 2 degrees of freedom vehicle model are established. The preview time is adaptively adjusted based on the preview model. Then, the vehicle trajectory tracking controller, which uses the terminal sliding mode algorithm, is designed. The radial basis function (RBF) neural network algorithm is used to approximate the system variable parameters in the control model online. At the same time, fuzzy logic is used to control the gain parameters of the controller to reduce the chattering of the control system. Finally, the designed controller is verified by simulation. The maximum deviation of path tracking under different speeds is 0.6 m, and the target path can also be well followed under different road friction coefficients. The simulation results show that the controller designed in this paper can effectively carry out the vehicle trajectory tracking and lateral control and reduce the chattering to a certain extent.\n

TAGHAVIFAR H , HU C , QIN Y C , et al . EKF-neural network observer based type-2 fuzzy control of autonomous vehicles [J ] . IEEE Transactions on Intelligent Transportation Systems , 2020 , 22 ( 8 ): 4788 - 4800 . DOI: 10.1109/TITS.2020.2985124 http://doi.org/10.1109/TITS.2020.2985124 https://ieeexplore.ieee.org/document/9067077/ https://ieeexplore.ieee.org/document/9067077/

HU J Q , ZHANG Y M , SUBHASH R . Adaptive trajectory tracking for carlike vehicles with input constraints [J ] . IEEE Transactions on Industrial Electronics , 2022 , 69 ( 3 ): 2801 - 2810 . DOI: 10.1109/TIE.2021.3068672 http://doi.org/10.1109/TIE.2021.3068672 https://ieeexplore.ieee.org/document/9390313/ https://ieeexplore.ieee.org/document/9390313/

孙志伟 , 李聪 . 基于径向基函数神经网络PID与模型预测控制的车辆轨迹跟踪控制 [J ] . 上海工程技术大学学报 , 2022 , 36 ( 2 ): 148 - 158 .

SUN Z W , LI C . Research on trajectory tracking control based on radial basis neural network PID and model predictive control [J ] . Journal of Shanghai University of Engineering Science , 2022 , 36 ( 2 ): 148 - 158 . (in Chinese)

YANG H J , WANG J , LI H B . Adaptive cooperative control for air-ground systems with actuator saturation under disturbances [J ] . International Journal of Adaptive Control and Signal Processing , 2022 , 37 ( 4 ): 880 - 914 . DOI: 10.1002/acs.v37.4 http://doi.org/10.1002/acs.v37.4 https://onlinelibrary.wiley.com/toc/10991115/37/4 https://onlinelibrary.wiley.com/toc/10991115/37/4

LU X H , JIA Y M . Adaptive coordinated control of uncertain free-floating space manipulators with prescribed control performance [J ] . Nonlinear Dynamics , 2019 , 97 : 1541 - 1566 . DOI: 10.1007/s11071-019-05071-w http://doi.org/10.1007/s11071-019-05071-w

BECHLIOULIS C P , ROVITHAKIS G A . Robust adaptive control of feedback linearizable MIMO nonlinear systems with prescribed performance [J ] . IEEE Transactions on Automatic Control , 2008 , 53 ( 9 ): 2090 - 2099 . DOI: 10.1109/TAC.2008.929402 http://doi.org/10.1109/TAC.2008.929402 http://ieeexplore.ieee.org/document/4639441/ http://ieeexplore.ieee.org/document/4639441/

LI Y M , TONG S C , LIU L , et al . Adaptive output-feedback control design with prescribed performance for switched nonlinear systems [J ] . Automatica , 2017 , 80 : 225 - 231 . DOI: 10.1016/j.automatica.2017.02.005 http://doi.org/10.1016/j.automatica.2017.02.005 https://linkinghub.elsevier.com/retrieve/pii/S0005109817300596 https://linkinghub.elsevier.com/retrieve/pii/S0005109817300596

XIA H Y , CHEN J Q , LAN F C , et al . Motion control of autonomous vehicles with guaranteed prescribed performance [J ] . International Journal of Adaptive Control and Signal Processing , 2019 , 18 : 1510 - 1517 .

江梦洁 , 李家旺 , 吕艳芳 , 等 . 饱和输入限制下欠驱动自主水下航行器水平面航迹跟踪控制 [J ] . 兵工学报 , 2017 , 38 ( 11 ): 2207 - 2213 . DOI: 10.3969/j.issn.1000-1093.2017.11.017 http://doi.org/10.3969/j.issn.1000-1093.2017.11.017 针对控制输入存在饱和限制的欠驱动自主水下航行器水平面航迹跟踪问题，提出了一种饱和 控制方法。在航迹跟踪误差方程基础上，设计了一种误差信号观测器对原有跟踪误差进行近似，以避免由于跟踪误差直接求导所引起的控制器表达式的复杂化现象；推导得到一种新的误差动力学方程，通过引入一种光滑有界函数作为输入饱和条件的近似，以及一种Nussbaum型偶函数，设计了饱和动力学控制器；根据Lyapunov理论证明了该控制器能够使得自主水下航行器在控制输入饱和限制下，可以实现对任意光滑水平面航迹的跟踪控制，并保证跟踪误差是全局最终一致有界的。仿真实验结果验证了该设计方法是有效的，且对于模型参数误差具有一定的鲁棒性。

JIANG M J , LI J W , LÜ Y F , et al . Path tracking control of underactuated autonomous underwater vehicles on horizontal plane within input saturation limit [J ] . Acta Armamentarii , 2017 , 38 ( 11 ): 2207 - 2213 . (in Chinese)

MA H , ZHOU Q , LI H Y , et al . Adaptive prescribed performance control of a flexible-joint robotic manipulator with dynamic uncertainties [J ] . IEEE Transactions on Cybernetics , 2022 , 52 ( 12 ): 12905 - 12915 . DOI: 10.1109/TCYB.2021.3091531 http://doi.org/10.1109/TCYB.2021.3091531 https://ieeexplore.ieee.org/document/9514367/ https://ieeexplore.ieee.org/document/9514367/

GUO K , PAN Y P , YU H Y . Composite learning robot control with friction compensation:a neural network-based approach [J ] . IEEE Transactions on Industrial Electronics , 2019 , 66 ( 10 ): 7841 - 7851 . DOI: 10.1109/TIE.41 http://doi.org/10.1109/TIE.41 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=41 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=41

吴艳 , 王丽芳 , 李芳 . 基于滑模自抗扰的智能车路径跟踪控制 [J ] . 控制与决策 , 2019 , 34 ( 10 ): 2150 - 2156 .

WU Y , WANG L F , LI F . Intelligent vehicle path following control based on sliding mode active disturbance rejection control [J ] . Control and Decision , 2019 , 34 ( 10 ): 2150 - 2156 . (in Chinese)