CN115629549B - 一种根据输入饱和的l2增益鲁棒路径跟踪方法 - Google Patents
一种根据输入饱和的l2增益鲁棒路径跟踪方法 Download PDFInfo
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Abstract
Description
技术领域
本发明属于无人驾驶技术领域,具体地,涉及一种根据输入饱和的L2增益鲁棒路径跟踪方法。
背景技术
人工智能时代的到来加速推动了智能汽车的发展。其中,无人驾驶技术是近年来学术界和工业界研究的热点问题。无人驾驶领域中的路径跟踪控制或转向控制问题是重要的研究课题,它涉及转向控制律的设计,以保证汽车能够跟踪由上层路径规划模块产生的参考路径。通常,通过路径跟踪控制模块可以将道路中心线与汽车位置的横向偏移距离降低到可接受的范围内。在这种系统中,控制输入是前轮转向角,控制目标是在考虑系统非线性、内部和外部干扰的情况下,让汽车尽可能平稳地沿着期望路径行驶。路径跟踪方法从模型类型可分为基于几何学、运动学和动力学的路径跟踪方法,基于几何学车辆模型的路径跟踪策略虽然结构简单、对参数依赖低。但是未考虑运动学和动力学特性,仅适用于车辆位置的跟踪。基于运动学的路径跟踪策略不需要过多依赖车身参数且易于实现,但其没有考虑车辆动力学特性,因此在车速过高和道路曲率变化过大的情况下无法保证汽车行驶稳定性和操纵性。基于动力学模型的路径跟踪策略有经典 PID 控制、最优控制、模糊逻辑控制、滑模控制、模型预测控制以及鲁棒控制。目前无人驾驶技术的应用场景局限于低速和封闭场景,如物流运输、共享出行、公共交通、环卫、港口码头以及矿山开采等领域。针对高速行驶无人驾驶场景的控制而言,一方面是研究高实时性控制方法来满足高速场景的应用需求。同时,针对汽车行驶过程中存在的外界干扰和内部参数不确定性,研究强鲁棒性路径跟踪方法来保证汽车行驶的平稳性也是十分有必要的。
发明内容
发明目的:本发明的目的是提供一种根据输入饱和的L2增益鲁棒路径跟踪方法,针对无人驾驶中路径跟踪方法要求高实时性和鲁棒性的需求,基于端口哈密尔顿系统和L2增益干扰消除方法,设计高实时性的自适应控制器,保证车辆在外部干扰下依然能够快速地跟踪期望路径。同时,设计的路径跟踪方法考虑了输入饱和的问题,避免控制性能受损。从理论上分析了设计的自适应控制器具备稳定性和鲁棒性。
技术方案:本发明提供了一种根据输入饱和的L2增益鲁棒路径跟踪方法,建立考虑外部干扰信号和输入饱和的端口哈密尔顿系统如下:
进一步的,上述的根据输入饱和的L2增益鲁棒路径跟踪方法,执行器的非线性给转向控制带来的影响如下:
进一步的,上述的根据输入饱和的L2增益鲁棒路径跟踪方法,进一步推导得到的不等式如下:
基于L2增益干扰消除理论,设计的自适应控制器如下:
上述技术方案可以看出,本发明具有如下有益效果:本发明所述的根据输入饱和的L2增益鲁棒路径跟踪方法,在仿真环境中验证考虑输入饱和的L2增益鲁棒路径跟踪方法的有效性;设计的自适应控制器能够克服外部干扰信号的影响,保证车辆能够有效地跟踪期望路径。
附图说明
图1为考虑输入饱和的L2增益鲁棒路径跟踪方法框图;
图2为双移线工况图;
图3为正弦波速度变化图;
图4为侧向偏差变化图;
图5为横摆角偏差变化图;
图6为横摆角速度变化图。
具体实施方式
如图1所示的根据输入饱和的L2增益鲁棒路径跟踪方法,建立考虑外部干扰信号和输入饱和的端口哈密尔顿系统如下:
执行器的非线性给转向控制带来的影响如下:
进一步推导得到的不等式如下:
基于L2增益干扰消除理论,设计的自适应控制器如下:
本发明方法在MATLAB和Carsim联合仿真平台上进行了验证,仿真中选取了双移线行驶工况,如图2所示。纵向车速 保持正弦波形式变化,如图3所示。此外,为了验证自适应控制器的鲁棒性,汽车质量和横摆惯量设置20%的浮动变化,路径跟踪效果如图4-6所示。仿真结果显示, 在速度变化、汽车质量和横摆惯量变化的驾驶环境下,侧向偏差、横摆角以及横摆角速度差值能够控制在较小范围内,表明设计的自适应控制器具有良好的鲁棒性,能够在参数变化下有效地跟踪期望路径。
以上所述仅是本发明的优选实施方式,应当指出,对于本技术领域的普通技术人员来说,在不脱离本发明原理的前提下,还可以做出若干改进,这些改进也应视为本发明的保护范围。
Claims (1)
1.一种根据输入饱和的L2增益鲁棒路径跟踪方法,其特征在于:建立考虑外部干扰信号和输入饱和的端口哈密尔顿系统如下:
执行器的非线性给转向控制带来的影响如下:
推导得到的不等式如下:
基于L2增益干扰消除理论,设计的自适应控制器如下:
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