摘要
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。
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.
作者
李睿
项昌乐
王超
范晶晶
刘春林
LI Rui;XIANG Changle;WANG Chao;FAN Jingjing;LIU Chunlin(School of Mechanical Engineering,Beijing Institute of Technology,Beijing 100081,China;Department of General Technology,China North Vehicle Research Institute,Beijing 100072,China;Department of Vehicle Chassis,China North Vehicle Research Institute,Beijing 100072,China;Intelligent Transportation Key Laboratory, North China University of Technology,Beijing 100144,China)
出处
《兵工学报》
EI
CAS
CSCD
北大核心
2021年第6期1128-1137,共10页
Acta Armamentarii
关键词
履带车辆
自动驾驶
鲁棒自适应
轨迹跟踪
tracked vehicle
autonomous driving
robust adaptivity
trajectory tracking
