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 PDF

Info

Publication number
CN115629549B
CN115629549B CN202211644462.8A CN202211644462A CN115629549B CN 115629549 B CN115629549 B CN 115629549B CN 202211644462 A CN202211644462 A CN 202211644462A CN 115629549 B CN115629549 B CN 115629549B
Authority
CN
China
Prior art keywords
follows
vehicle
tracking method
input saturation
path tracking
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202211644462.8A
Other languages
English (en)
Other versions
CN115629549A (zh
Inventor
马延
贺亮
陈建林
赵凯星
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Jiangsu Yunmu Zhizao Technology Co ltd
Taicang Yangtze River Delta Research Institute of Northwestern Polytechnical University
Original Assignee
Jiangsu Yunmu Zhizao Technology Co ltd
Taicang Yangtze River Delta Research Institute of Northwestern Polytechnical University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Jiangsu Yunmu Zhizao Technology Co ltd, Taicang Yangtze River Delta Research Institute of Northwestern Polytechnical University filed Critical Jiangsu Yunmu Zhizao Technology Co ltd
Priority to CN202211644462.8A priority Critical patent/CN115629549B/zh
Publication of CN115629549A publication Critical patent/CN115629549A/zh
Application granted granted Critical
Publication of CN115629549B publication Critical patent/CN115629549B/zh
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02TCLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Feedback Control In General (AREA)

Abstract

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:
Figure 232265DEST_PATH_IMAGE001
;其中,
Figure 598656DEST_PATH_IMAGE002
为系统状态变量
Figure 125583DEST_PATH_IMAGE003
的导数;系统状态量
Figure 46266DEST_PATH_IMAGE004
Figure 316841DEST_PATH_IMAGE005
为车辆质心与期望路径的侧向偏差;
Figure 741001DEST_PATH_IMAGE006
为车辆质心与期望路径的侧向偏差的导数;
Figure 704409DEST_PATH_IMAGE007
为实际横摆角和期望横摆角差值;
Figure 846808DEST_PATH_IMAGE008
为实际横摆角速度和期望横摆角速度的差值。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。

Description

一种根据输入饱和的L2增益鲁棒路径跟踪方法
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:
Figure 931221DEST_PATH_IMAGE001
其中,
Figure 944308DEST_PATH_IMAGE002
为系统状态变量
Figure 547459DEST_PATH_IMAGE003
的导数;
系统状态量
Figure 911575DEST_PATH_IMAGE004
Figure 523953DEST_PATH_IMAGE005
为车辆质心与期望路径的侧向偏差;
Figure 188284DEST_PATH_IMAGE006
为车辆质心与期望路径的侧向偏差的导数;
Figure 493494DEST_PATH_IMAGE007
为实际横摆角和期望横摆角差值;
Figure 610486DEST_PATH_IMAGE008
为实际横摆角速度和期望横摆角速度的差值。
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,上述互联矩阵
Figure 823293DEST_PATH_IMAGE009
和阻尼矩阵
Figure 342130DEST_PATH_IMAGE010
满足:
Figure 818242DEST_PATH_IMAGE011
Figure 422530DEST_PATH_IMAGE012
,表示如下:
Figure 907869DEST_PATH_IMAGE013
Figure 39071DEST_PATH_IMAGE014
其中 ,
Figure 686084DEST_PATH_IMAGE015
Figure 43247DEST_PATH_IMAGE016
分别为前轮和后轮的侧偏刚度;
Figure 66698DEST_PATH_IMAGE017
Figure 294548DEST_PATH_IMAGE018
分别为汽车质心到前后轴的距离;
Figure 643621DEST_PATH_IMAGE019
为横摆惯量;
Figure 956921DEST_PATH_IMAGE020
为纵向车速;
Figure 315222DEST_PATH_IMAGE021
为汽车质量。
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,端口哈密尔顿函数
Figure 663157DEST_PATH_IMAGE022
定义如下:
Figure 386394DEST_PATH_IMAGE023
其中,
Figure 718149DEST_PATH_IMAGE024
为饱和函数;
Figure 552244DEST_PATH_IMAGE025
为前轮转向角;
Figure 20266DEST_PATH_IMAGE026
为外部干扰信号。
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,控制矩阵
Figure 914404DEST_PATH_IMAGE027
和干扰矩阵
Figure 733455DEST_PATH_IMAGE028
分别表示如下:
Figure 902399DEST_PATH_IMAGE029
Figure 959348DEST_PATH_IMAGE030
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:
Figure 555546DEST_PATH_IMAGE031
其中,
Figure 65156DEST_PATH_IMAGE032
为前轮转角的最大值。
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,定义变量
Figure 772212DEST_PATH_IMAGE033
如下:
Figure 480405DEST_PATH_IMAGE034
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:
Figure 247503DEST_PATH_IMAGE035
存在权重参数
Figure 41147DEST_PATH_IMAGE036
使得下面的不等式成立
Figure 286315DEST_PATH_IMAGE037
其中,参数
Figure 583435DEST_PATH_IMAGE038
是给定的干扰消除程度;
引入惩罚信号
Figure 990277DEST_PATH_IMAGE039
如下:
Figure 5637DEST_PATH_IMAGE040
基于L2增益干扰消除理论,设计的自适应控制器如下:
Figure 788917DEST_PATH_IMAGE041
将设计的前轮转向角代入端口哈密尔顿系统,并结合上述不等式,可推导得到
Figure 206123DEST_PATH_IMAGE042
耗散不等式成立如下:
Figure 580603DEST_PATH_IMAGE043
其中,
Figure 286522DEST_PATH_IMAGE044
为欧几里德范数,由
Figure 139072DEST_PATH_IMAGE045
耗散不等式成立表明设计的自适应控制器具有稳定性和鲁棒性。
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:
Figure 879626DEST_PATH_IMAGE001
其中,
Figure 893849DEST_PATH_IMAGE002
为系统状态变量
Figure 618223DEST_PATH_IMAGE003
的导数;
系统状态量
Figure 985446DEST_PATH_IMAGE004
Figure 580507DEST_PATH_IMAGE046
为车辆质心与期望路径的侧向偏差;
Figure 296790DEST_PATH_IMAGE047
为车辆质心与期望路径的侧向偏差的导数;
Figure 508460DEST_PATH_IMAGE048
为实际横摆角和期望横摆角差值;
Figure 968391DEST_PATH_IMAGE049
为实际横摆角速度和期望横摆角速度的差值。
上述互联矩阵
Figure 949117DEST_PATH_IMAGE009
和阻尼矩阵
Figure 570722DEST_PATH_IMAGE010
满足:
Figure 4108DEST_PATH_IMAGE011
Figure 736572DEST_PATH_IMAGE012
,表示如下:
Figure 40646DEST_PATH_IMAGE050
Figure 98732DEST_PATH_IMAGE051
其中,
Figure 19414DEST_PATH_IMAGE052
Figure 821148DEST_PATH_IMAGE053
分别为前轮和后轮的侧偏刚度;
Figure 42045DEST_PATH_IMAGE054
Figure 739874DEST_PATH_IMAGE055
分别为汽车质心到前后轴的距离;
Figure 616694DEST_PATH_IMAGE056
为横摆惯量;
Figure 690960DEST_PATH_IMAGE057
为纵向车速;
Figure 500785DEST_PATH_IMAGE058
为汽车质量。
端口哈密尔顿函数
Figure 103935DEST_PATH_IMAGE059
定义如下:
Figure 468052DEST_PATH_IMAGE023
其中,
Figure 814851DEST_PATH_IMAGE060
为饱和函数;
Figure 479181DEST_PATH_IMAGE061
为前轮转向角;
Figure 49971DEST_PATH_IMAGE062
为外部干扰信号。
控制矩阵
Figure 166963DEST_PATH_IMAGE063
和干扰矩阵
Figure 848611DEST_PATH_IMAGE064
分别表示如下:
Figure 633027DEST_PATH_IMAGE029
Figure 109139DEST_PATH_IMAGE030
执行器的非线性给转向控制带来的影响如下:
Figure 713427DEST_PATH_IMAGE031
其中,
Figure 198766DEST_PATH_IMAGE032
为前轮转角的最大值。
定义变量
Figure 572110DEST_PATH_IMAGE065
如下:
Figure 15860DEST_PATH_IMAGE034
进一步推导得到的不等式如下:
Figure 373024DEST_PATH_IMAGE035
存在权重参数
Figure 130895DEST_PATH_IMAGE066
使得下面的不等式成立
Figure 624325DEST_PATH_IMAGE037
其中,参数
Figure 442239DEST_PATH_IMAGE045
是给定的干扰消除程度;
引入惩罚信号
Figure 357056DEST_PATH_IMAGE067
如下:
Figure 184198DEST_PATH_IMAGE040
基于L2增益干扰消除理论,设计的自适应控制器如下:
Figure 797713DEST_PATH_IMAGE041
将设计的前轮转向角代入端口哈密尔顿系统,并结合上述不等式,可推导得到
Figure 317687DEST_PATH_IMAGE045
耗散不等式成立如下:
Figure 587126DEST_PATH_IMAGE043
其中,
Figure 952379DEST_PATH_IMAGE044
为欧几里德范数,由
Figure 889242DEST_PATH_IMAGE045
耗散不等式成立表明设计的自适应控制器具有稳定性和鲁棒性。
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
Figure 580118DEST_PATH_IMAGE057
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:
Figure QLYQS_1
其中,
Figure QLYQS_2
为系统状态变量
Figure QLYQS_3
的导数;
系统状态量
Figure QLYQS_4
Figure QLYQS_5
为车辆质心与期望路径的侧向偏差;
Figure QLYQS_6
为车辆质心与期望路径的侧向偏差的导数;
Figure QLYQS_7
为实际横摆角和期望横摆角差值;
Figure QLYQS_8
为实际横摆角速度和期望横摆角速度的差值;
互联矩阵
Figure QLYQS_9
Figure QLYQS_10
Figure QLYQS_11
满足:
Figure QLYQS_12
Figure QLYQS_13
,表示如下:
Figure QLYQS_14
Figure QLYQS_15
其中,
Figure QLYQS_16
Figure QLYQS_17
分别为前轮和后轮的侧偏刚度;
Figure QLYQS_18
Figure QLYQS_19
分别为汽车质心到前后轴的距离;
Figure QLYQS_20
为横摆惯量;
Figure QLYQS_21
为纵向车速;
Figure QLYQS_22
为汽车质量;
端口哈密尔顿函数
Figure QLYQS_23
定义如下:
Figure QLYQS_24
其中,
Figure QLYQS_25
为饱和函数;
Figure QLYQS_26
为前轮转向角;
Figure QLYQS_27
为外部干扰信号;
控制矩阵
Figure QLYQS_28
和干扰矩阵
Figure QLYQS_29
分别表示如下:
Figure QLYQS_30
Figure QLYQS_31
执行器的非线性给转向控制带来的影响如下:
Figure QLYQS_32
其中,
Figure QLYQS_33
为前轮转角的最大值;
定义变量
Figure QLYQS_34
如下:
Figure QLYQS_35
推导得到的不等式如下:
Figure QLYQS_36
存在权重参数
Figure QLYQS_37
使得下面的不等式成立
Figure QLYQS_38
其中,参数
Figure QLYQS_39
是给定的干扰消除程度;
引入惩罚信号
Figure QLYQS_40
如下:
Figure QLYQS_41
基于L2增益干扰消除理论,设计的自适应控制器如下:
Figure QLYQS_42
将设计的前轮转向角代入端口哈密尔顿系统,并结合上述不等式,可推导得到
Figure QLYQS_43
耗散不等式成立如下:
Figure QLYQS_44
其中,
Figure QLYQS_45
为欧几里德范数,由
Figure QLYQS_46
耗散不等式成立表明设计的自适应控制器具有稳定性和鲁棒性。
CN202211644462.8A 2022-12-21 2022-12-21 一种根据输入饱和的l2增益鲁棒路径跟踪方法 Active CN115629549B (zh)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202211644462.8A CN115629549B (zh) 2022-12-21 2022-12-21 一种根据输入饱和的l2增益鲁棒路径跟踪方法

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202211644462.8A CN115629549B (zh) 2022-12-21 2022-12-21 一种根据输入饱和的l2增益鲁棒路径跟踪方法

Publications (2)

Publication Number Publication Date
CN115629549A CN115629549A (zh) 2023-01-20
CN115629549B true CN115629549B (zh) 2023-04-04

Family

ID=84910756

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202211644462.8A Active CN115629549B (zh) 2022-12-21 2022-12-21 一种根据输入饱和的l2增益鲁棒路径跟踪方法

Country Status (1)

Country Link
CN (1) CN115629549B (zh)

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102326127A (zh) * 2009-02-18 2012-01-18 丰田自动车株式会社 状态反馈控制装置、状态反馈控制器以及状态反馈控制方法
US9296474B1 (en) * 2012-08-06 2016-03-29 The United States of America as represented by the Administrator of the National Aeronautics & Space Administration (NASA) Control systems with normalized and covariance adaptation by optimal control modification
CN110908281A (zh) * 2019-11-29 2020-03-24 天津大学 无人直升机姿态运动有限时间收敛强化学习控制方法
CN111679585B (zh) * 2020-07-03 2022-08-26 大连海事大学 一种具有输入饱和受限的无人船强化学习自适应跟踪控制方法
CN114442640B (zh) * 2022-02-28 2022-09-16 哈尔滨理工大学 一种水面无人艇轨迹跟踪控制方法

Also Published As

Publication number Publication date
CN115629549A (zh) 2023-01-20

Similar Documents

Publication Publication Date Title
Hu et al. Output constraint control on path following of four-wheel independently actuated autonomous ground vehicles
CN107992681B (zh) 一种电动汽车主动前轮转向系统的复合控制方法
Yu et al. MPC-based regional path tracking controller design for autonomous ground vehicles
CN113911106B (zh) 基于博弈论的商用车横向轨迹跟随与稳定性协同控制方法
WO2023024914A1 (zh) 车辆避让方法、装置、计算机设备和存储介质
Hang et al. Path-tracking controller design for a 4WIS and 4WID electric vehicle with steer-by-wire system
Yuan et al. Research on model predictive control-based trajectory tracking for unmanned vehicles
CN111679575A (zh) 一种基于鲁棒模型预测控制的智能汽车轨迹跟踪控制器及其构造方法
CN116560371A (zh) 基于自适应模型预测控制的自动驾驶车辆路径跟踪方法
Li et al. Adaptive sliding mode control of lateral stability of four wheel hub electric vehicles
Hang et al. Design of an active collision avoidance system for a 4WIS-4WID electric vehicle
Wang et al. Robust trajectory tracking control for autonomous vehicle subject to velocity-varying and uncertain lateral disturbance
Fergani et al. Full vehicle dynamics control based on LPV/ℋ∞ and flatness approaches
Nan et al. MPC-based path tracking control with forward compensation for autonomous driving
CN115629549B (zh) 一种根据输入饱和的l2增益鲁棒路径跟踪方法
CN117826590A (zh) 基于前置跟随拓扑结构的无人车编队控制方法及系统
Li et al. Study on steering angle input during the automated lane change of electric vehicle
CN115214697A (zh) 一种自适应二阶滑模控制智能汽车横向控制方法
Chen et al. Ribbon model based path tracking method for autonomous ground vehicles
Németh et al. Set-based analysis of the variable-geometry suspension system
CHEN et al. A review of autonomous vehicle path tracking algorithm research
CN114044003A (zh) 前后双轴转向车辆的循迹控制方法
Hou et al. Integrated chassis control using ANFIS
Sun et al. Design of Four Wheel Steering and Direct Yaw Moment Control for Unmanned Vehicle with Path Tracking Controller in Extreme Maneuvers
Siampis Optimal torque vectoring control strategies for stabilisation of electric vehicles at the limits of handling

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant