基于蚁狮算法优化的LQR横向跟踪控制策略

LQR lateral tracking control strategy based on ALO algorithm

为解决线性二次型调节器(LQR)在经典固定权重系数下对大曲率参考路径适应性不佳致使车辆跟踪精度与稳定性欠佳的问题,设计了一种基于蚁狮算法(ALO)优化的带有预瞄前馈转角补偿的自适应权重系数LQR控制器以进行路径横向跟踪。基于2自由度车辆动力学横向跟踪误差模型设计了经典LQR控制器。采用预瞄前馈控制消除误差模型简化带来的稳态误差。提出以横向距离偏差、航向角偏差和输出前轮转角为评价函数,基于蚁狮算法优化的自适应LQR权重系数修正策略。通过实车测验,验证了控制器在实车环境下的控制效果。结果表明:所设计的控制器能够适应大曲率参考路径,并兼顾路径跟踪精准性和行驶稳定性,同时针对不同车速鲁棒性表现优异。

In order to solve the problem of poor vehicle tracking accuracy and poor stability caused by poor adaptability of a linear quadratic regulator(LQR)to the large curvature reference path under the traditional fixed weight coefficient,this paper designs an adaptive weight coefficient LQR controller with preview feedforward angle compensation to track the path laterally based on ant lion optimization(ALO)algorithm.Firstly,a classical LQR controller is designed based on the lateral tracking error model of two-degree-of-freedom vehicle dynamics.Secondly,the preview feedforward control is used to eliminate the steady state error caused by error model simplification.Then,an adaptive LQR weight coefficient correction strategy based on ALO is proposed,which takes lateral distance deviation,heading angle deviation and output front wheel angle as the evaluation functions.Finally,through the real vehicle test,the control effect of the controller in the real vehicle environment is verified.The results show that the designed controller can adapt to the large curvature reference path,take into account of the path tracking accuracy and driving stability,and perform well in robustness at different vehicle speeds.