自动驾驶四轮独立驱动电动汽车的自适应分层轨迹跟踪控制方法(七)

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B、实验结果

所提出的轨迹跟踪控制系统在一辆原型自动驾驶轮内电机4WID电动车辆上实施并成功测试,原型车如图17所示。

选择四个永磁无刷直流(BLDC)电动机作为轮内电动机。角位移传感器用于测量车轮的转向角。横摆率,纵向速度和滑移角等车辆状态量由GPS / INS导航系统精确测量和估算[28],[29]。原型车配备实时视觉系统,由两个CCD摄像头和一个基于PC的中央处理系统组成,视觉系统的处理时间小于每帧20ms。值得一提的是,视觉系统可以实时检测预定的跟踪轨迹并精确确定横向误差和角度误差[15]。道路附着力估计器的带宽为25Hz [27],控制器的采样间隔限制为40ms。图18示出了在实验测试中使用的参考轨迹。相应的初始横向误差和偏航误差分别设定为0.1m和2deg,纵向速度假设为25km/ h。

图19显示了横向误差的实验结果,可以看出所提出的控制方案和LQR控制方案的稳态横向误差分别限制在±0.2m和±0.4m之内,最大横向误差发生在曲率最大的路段。图20示出了角度误差的实验结果,应注意所提出的控制方案和LQR控制方案的稳态角度误差分别在±1°和±2°范围内。图19和图20表明,所提出的AFSMC控制器可以确保自动驾驶车辆实时跟踪参考轨迹,并且与LQR控制器相比,它产生更高的精度和更低的超调量和振荡。图21和图22示出了滑移角和横摆率的响应结果,它们表明所提出的控制器和LQR控制器可以分别将滑移角和横摆率限制在可接受的范围内。然而,所提出的控制系统显着提高了响应精度。图23示出了比较的前转向角,可以看出所提出的控制方案的控制输入比LQR控制器更平滑。图24显示了所提出的控制方法的外部横摆力矩。可以看出,所提出的控制方案可以实时产生外部横摆力矩,这可以增强自动驾驶车辆的横向稳定性。

5.结论

本文提出了一种新的四轮独立驱动自动驾驶汽车的自适应分层轨迹跟踪控制方案。首先,提出了一种基于LMI的自适应滑模高级控制算法,用于确定自动驾驶车辆的前转向和外横摆力矩矢量。由于参数不确定性和外部扰动通常是不可测量的,因此通过模糊控制系统估算所提出的高级控制律的不确定项和控制增益,并引入自适应模糊边界层。然后,设计伪逆控制分配策略以将期望的外部横摆力矩动态地分配到冗余轮胎致动器中。此外,仿真和实验结果表明,所提出的控制方案可以在不同的驱动条件下实现良好的跟踪性能。

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全文完结

来源:同济智能汽车研究所

 

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