视觉导航式智能车辆横向与纵向控制研究
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摘要
智能车辆(Intelligent Vehicle, Ⅳ)是车辆工程领域的研究前沿和未来汽车工业发展的新方向,其智能化集中体现在自动驾驶方面,是许多高新技术综合集成的载体。严峻的交通安全问题使得智能车辆作为智能交通系统(Intelligent Transportation System,ITS)的一个重要组成部分日益受到重视。
运动控制是智能车辆研究领域中的核心问题之一,是其他相关研究的基础,且始终是智能车辆研究领域的热点,其研究内容主要包括横向控制和纵向控制。由于智能车辆为非完整运动约束系统,且具有高度非线性动态特性以及参数的不确定性等特点,因此如何设计可有效克服车辆非线性和参数不确定性等特性的横向及纵向运动控制策略,便成为实现智能车辆自主导航的重点和难点,具有深远的研究意义。针对此问题,本文以大连理工大学汽车工程学院自主研制开发的DUTIV-Ⅰ实验型智能车辆为试验研究平台,开展了智能车辆横向及纵向运动控制方法的研究,主要工作归纳如下：
(1)设计视觉系统获取路径信息的图像处理算法
阐述了DUTIV-Ⅰ智能车辆视觉系统的总体设计方案,探讨了DUTIV-Ⅰ智能车辆视觉系统CCD摄像机相关参数的选取原则,设计了智能车辆视觉系统获取路径信息的图像处理算法,为智能车辆导航控制提供了准确的路径信息。
(2)建立用于横向及纵向运动控制系统仿真研究的车辆动力学模型
针对智能车辆横向及纵向运动控制研究的特点以及本文试验车辆的结构,以试验车辆的基本数据为基础,采用分层模块化的思想建立了能够反映智能车辆主要运动状态、轮胎模型耦合特性和传动系统非线性特性的15自由度非线性车辆模型,包括：发动机模型、液力变矩器及自动变速器模型、制动系统模型、传动系统模型、车轮动力学模型、轮胎模型、整车模型、悬架模型、执行器模型。通过典型工况仿真与实车试验对比对本文建立的车辆模型进行了模型验证,结果表明,本文建立的车辆仿真模型可以准确描述车辆的动力学特性,具有较高的仿真精度,为智能车辆横向及纵向控制研究提供了准确的模型基础。
(3)基于遗传算法的智能车辆横向模糊控制研究
以视觉导航式智能车辆为研究对象,建立了视觉导航式智能车辆的横向控制模型,采用根轨迹法分析了预瞄距离和速度对横向控制系统的影响,设计了预瞄距离是速度一次函数的计算模型,从而优化了视觉系统获取的路径信息数据。针对智能车辆具有高度非线性动态特性及参数不确定性等特点,设计了由用于补偿参考路径曲率扰动的前馈控制器和模糊反馈控制器组成的可模拟人类驾驶行为的横向控制系统。考虑到采用试探方法或不断积累的经验来确定模糊控制隶属度函数参数和控制规则,难以根据系统特性进行自适应调整,易产生稳态误差,提出基于遗传算法的智能车辆模糊反馈控制策略,通过遗传算法对模糊反馈控制器的隶属度函数参数和控制规则进行自动优化,有效地确定出模糊反馈控制器的隶属度函数和控制规则。采用Popov-Lyapunov方法证明了横向模糊反馈控制系统的稳定性。在基于遗传优化的模糊反馈控制器的基础上,提出了基于速度分区的分层控制系统,提高了横向控制系统对速度变化的自适应能力。仿真结果表明,本文提出的横向控制算法具有良好的动态和稳态性能。
(4)智能车辆纵向自适应模糊滑模控制研究
首先建立用于纵向控制系统设计的智能车辆非线性纵向动力学控制模型；其次,采用经典PID控制方法设计纵向控制系统的上位控制器；再次,在完成纵向控制系统上位控制器设计的基础上,针对纵向动力学模型具有较大的参数不确定性、外界干扰和时滞等特性,采用自适应模糊滑模控制(Adaptive Fuzzy Logic Slide Mode Control, AFLSMC)方法设计纵向控制系统的下位控制器,利用模糊逻辑调节滑模控制增益系数和边界层厚度,可有效提高控制器的动态性能,实现对速度的快速和准确的跟踪控制；然后,基于Lyapunov方法对纵向控制系统的稳定性进行了证明,可保证纵向控制系统的稳定性。最后,构造了油门控制与制动控制的切换标准,避免油门执行器和制动执行器之间的频繁切换。仿真结果表明,本文所提出的纵向控制算法可保证油门执行器与制动执行器之间的平滑过渡,且能够实现智能车辆纵向模型存在较大参数不确定性和外部干扰时纵向运动合理、准确控制。
(5)横向控制算法与纵向控制算法的实车试验
为进一步验证文中所提出方法的有效性和准确性,应用课题组研发的DUTIV-Ⅰ智能车辆实验平台,针对横向路径跟踪控制和纵向速度跟踪控制中的部分典型工况,分别对文中设计的智能车辆横向运动控制系统和纵向运动控制系统性能进行试验研究。结果表明,基于本文方法所完成的智能车辆横向运动控制系统和纵向运动控制系统表现出良好的动态性能、稳态跟踪精度和鲁棒性能。
Intelligent Vehicles (IV)which are the integrated carriers of many advanced high-new technologies, are the research frontier of the vehicle engineering and will become a new direction for future development of automobile industry, and their intelligence is mainly incarnated in the automatic navigation. As an important subsystem of the Intelligent Transportation System (ITS), intelligent vehicles have attracted more and more attentions due to the critical traffic safety problems.
Navigation control is a key problem in the field of intelligent vehicles research, and it is a research hotspot and the basis of other relevant research. The research contents of navigation control mainly include lateral motion control and longitudinal motion control. Because intelligent vehicles are the nonholonomic motion constraint system, and have highly nonlinear dynamic characteristics and parametric uncertain properties, therefore, how to design the lateral and longitudinal motion control strategy, which can effectively overcome the characteristics of vehicle nonlinear and parameter uncertainties, is the emphasis and difficulty of IV automatic driving and has profound research significance. To solve this problem, in this paper, lateral and longitudinal motion control of intelligent vehicles are studied based on a prototype intelligent vehicle DUTIV-I, which is taken as the platform for experimental research and developed by the School of Automotive Engineering of Dalian University of Technology. The research work of this thesis consists of the following major parts:
(1) Image processing algorithms of path information obtained by vision system are designed
An overall design scheme of DUTIV-I's vision system is descirbed, the selection principle of relevant parameters for CCD camera of DUTIV-I's vision system is explored, and image processing algorithms of path information obtained by vision system are designed to provide accurate path information for intelligent vehicle navigation control.
(2) A vehicle dynamic model is established for simulation research on lateral and longitudinal control system
In view of the characteristic features of lateral motion control and longitudinal motion control of intelligent vehicle and the structure of the prototype vehicle DUTIV-I, a15degree-of-freedom nonlinear vehicle model which can accurately reflects the vehicle main movement states, the coupling characteristics of the tire model and the nonlinear characteristics of transmission system, is constructed by modularization method based on the basis data of the prototype vehicle. It is composed of engine model, hydraulic torque converter model, automatic transmission model,power train model, brake system model, wheel dynamics model, tire model, vehicle body model, suspension model and actuators model. The verification of established vehicle model is carried out by the simulation and experimental comparison under typical working conditions. The results show that the established vehicle model can accurately describe the vehicle dynamical characteristics and has the high simulation precision, which provides a reliable foundation for researching on lateral and longitudinal control.
(3) Study on lateral fuzzy control of intelligent vehicle using genetic algorithms.
A vision-based navigation of intelligent vehicle is taken as research object, and the lateral motion control model of vision-based navigation intelligent vehicle is established, which can describe the lateral motion behavior of intelligent vehicles. On this basis, the influence of look-ahead distance and speed on lateral control system is analyzed using root locus method, and the formula of look-ahead distance as a linear function of speed is established, which can optimize the path information data obtained by vision system. Aiming at highly nonlinear and parametric uncertain properties of non-holonomic intelligent vehicles, an automatic lateral controller consisting of a fuzzy feedback control law which can imitate human driving behavior and a feed-forward control law used to offset the disturbance of the curvature of reference paths is designed. In view of the membership functions and rules base designed either by experts knowledge, or iteratively by trial-and error are difficult to adaptive adjust according to the system characteristics and easy to produce overshoot or/and steady-state error. To confront this difficulty, the lateral fuzzy feedback control strategy of intelligent vehicles based on genetic algorithms is proposed. The membership functions and rules base of fuzzy feedback control law are automatically optimized and effectively determined by genetic algorithms. The stability of this fuzzy feedback control system is proved by Popov-Lyapunov. On this basis, the hierarchical control structure of fuzzy feedback control law according to driving speed, and in different layers fuzzy feedback controller is designed via genetic algorithms to optimize the parameters of membership functions and rules, which can effectively improve the adaptive ability lateral control system to the change of speed. The simulation results demonstrate the lateral control system proposed by this paper has better stable and dynamic performance.
(4) Study on longitudinal adaptive sliding-mode control of intelligent vehicle using via fuzzy-logic.
Firstly, the longitudinal nonlinear dynamic control model of intelligent vehicle is established which is used for the design of longitudinal control system. Secondly, the upper level controller of longitudinal control system is designed by classic PID control method. On the basis of upper level controller, aiming at the large parametric uncertain, external disturbance and time delay properties of longitudinal dynamic control model, the lower level controller of longitudinal control system is designed by adaptive fuzzy sliding mode control method, and the gain coefficients and boundary layers of sliding-mode control are adaptive tuned by fuzzy logic, which can effectively improve the performance and eliminate the chattering phenomenon, consequently, the vehicle speed can be controlled rapidly and exactly by proposed method. Then, the stability of the closed-loop longitudinal control system is proved by Lyapunov method. Finally, the switching criterion between the throttle actuator and brake actuator is presented, which can guarantee the smooth switching between throttle actuator and brake actuator. The simulation results demonstrate that the proposed control algorithm can guarantee the smooth transitions between throttle actuator and brake actuator and implement the rational and accurate control of longitudinal motion in presence of large model uncertainties and external distribution.
(5) Experimental study on lateral and longitudinal control algorithms proposed by this paper.
To further verify the effectiveness and accuracy of lateral and longitudinal control algorithms proposed this paper, aiming at the typical working conditions of lateral path tracking control and longitudinal speed tracking, the experimental tests of lateral motion control system and longitudinal motion control system of intelligent vehicle are carried out using the prototype vehicle DUTIV-I, respectively. The experimental results indicate that the lateral motion control system and longitudinal motion control system based on proposed method exhibit the better characteristics dynamic of dynamic response, tracking precision and robustness.
运动控制是智能车辆研究领域中的核心问题之一,是其他相关研究的基础,且始终是智能车辆研究领域的热点,其研究内容主要包括横向控制和纵向控制。由于智能车辆为非完整运动约束系统,且具有高度非线性动态特性以及参数的不确定性等特点,因此如何设计可有效克服车辆非线性和参数不确定性等特性的横向及纵向运动控制策略,便成为实现智能车辆自主导航的重点和难点,具有深远的研究意义。针对此问题,本文以大连理工大学汽车工程学院自主研制开发的DUTIV-Ⅰ实验型智能车辆为试验研究平台,开展了智能车辆横向及纵向运动控制方法的研究,主要工作归纳如下：
(1)设计视觉系统获取路径信息的图像处理算法
阐述了DUTIV-Ⅰ智能车辆视觉系统的总体设计方案,探讨了DUTIV-Ⅰ智能车辆视觉系统CCD摄像机相关参数的选取原则,设计了智能车辆视觉系统获取路径信息的图像处理算法,为智能车辆导航控制提供了准确的路径信息。
(2)建立用于横向及纵向运动控制系统仿真研究的车辆动力学模型
针对智能车辆横向及纵向运动控制研究的特点以及本文试验车辆的结构,以试验车辆的基本数据为基础,采用分层模块化的思想建立了能够反映智能车辆主要运动状态、轮胎模型耦合特性和传动系统非线性特性的15自由度非线性车辆模型,包括：发动机模型、液力变矩器及自动变速器模型、制动系统模型、传动系统模型、车轮动力学模型、轮胎模型、整车模型、悬架模型、执行器模型。通过典型工况仿真与实车试验对比对本文建立的车辆模型进行了模型验证,结果表明,本文建立的车辆仿真模型可以准确描述车辆的动力学特性,具有较高的仿真精度,为智能车辆横向及纵向控制研究提供了准确的模型基础。
(3)基于遗传算法的智能车辆横向模糊控制研究
以视觉导航式智能车辆为研究对象,建立了视觉导航式智能车辆的横向控制模型,采用根轨迹法分析了预瞄距离和速度对横向控制系统的影响,设计了预瞄距离是速度一次函数的计算模型,从而优化了视觉系统获取的路径信息数据。针对智能车辆具有高度非线性动态特性及参数不确定性等特点,设计了由用于补偿参考路径曲率扰动的前馈控制器和模糊反馈控制器组成的可模拟人类驾驶行为的横向控制系统。考虑到采用试探方法或不断积累的经验来确定模糊控制隶属度函数参数和控制规则,难以根据系统特性进行自适应调整,易产生稳态误差,提出基于遗传算法的智能车辆模糊反馈控制策略,通过遗传算法对模糊反馈控制器的隶属度函数参数和控制规则进行自动优化,有效地确定出模糊反馈控制器的隶属度函数和控制规则。采用Popov-Lyapunov方法证明了横向模糊反馈控制系统的稳定性。在基于遗传优化的模糊反馈控制器的基础上,提出了基于速度分区的分层控制系统,提高了横向控制系统对速度变化的自适应能力。仿真结果表明,本文提出的横向控制算法具有良好的动态和稳态性能。
(4)智能车辆纵向自适应模糊滑模控制研究
首先建立用于纵向控制系统设计的智能车辆非线性纵向动力学控制模型；其次,采用经典PID控制方法设计纵向控制系统的上位控制器；再次,在完成纵向控制系统上位控制器设计的基础上,针对纵向动力学模型具有较大的参数不确定性、外界干扰和时滞等特性,采用自适应模糊滑模控制(Adaptive Fuzzy Logic Slide Mode Control, AFLSMC)方法设计纵向控制系统的下位控制器,利用模糊逻辑调节滑模控制增益系数和边界层厚度,可有效提高控制器的动态性能,实现对速度的快速和准确的跟踪控制；然后,基于Lyapunov方法对纵向控制系统的稳定性进行了证明,可保证纵向控制系统的稳定性。最后,构造了油门控制与制动控制的切换标准,避免油门执行器和制动执行器之间的频繁切换。仿真结果表明,本文所提出的纵向控制算法可保证油门执行器与制动执行器之间的平滑过渡,且能够实现智能车辆纵向模型存在较大参数不确定性和外部干扰时纵向运动合理、准确控制。
(5)横向控制算法与纵向控制算法的实车试验
为进一步验证文中所提出方法的有效性和准确性,应用课题组研发的DUTIV-Ⅰ智能车辆实验平台,针对横向路径跟踪控制和纵向速度跟踪控制中的部分典型工况,分别对文中设计的智能车辆横向运动控制系统和纵向运动控制系统性能进行试验研究。结果表明,基于本文方法所完成的智能车辆横向运动控制系统和纵向运动控制系统表现出良好的动态性能、稳态跟踪精度和鲁棒性能。
Intelligent Vehicles (IV)which are the integrated carriers of many advanced high-new technologies, are the research frontier of the vehicle engineering and will become a new direction for future development of automobile industry, and their intelligence is mainly incarnated in the automatic navigation. As an important subsystem of the Intelligent Transportation System (ITS), intelligent vehicles have attracted more and more attentions due to the critical traffic safety problems.
Navigation control is a key problem in the field of intelligent vehicles research, and it is a research hotspot and the basis of other relevant research. The research contents of navigation control mainly include lateral motion control and longitudinal motion control. Because intelligent vehicles are the nonholonomic motion constraint system, and have highly nonlinear dynamic characteristics and parametric uncertain properties, therefore, how to design the lateral and longitudinal motion control strategy, which can effectively overcome the characteristics of vehicle nonlinear and parameter uncertainties, is the emphasis and difficulty of IV automatic driving and has profound research significance. To solve this problem, in this paper, lateral and longitudinal motion control of intelligent vehicles are studied based on a prototype intelligent vehicle DUTIV-I, which is taken as the platform for experimental research and developed by the School of Automotive Engineering of Dalian University of Technology. The research work of this thesis consists of the following major parts:
(1) Image processing algorithms of path information obtained by vision system are designed
An overall design scheme of DUTIV-I's vision system is descirbed, the selection principle of relevant parameters for CCD camera of DUTIV-I's vision system is explored, and image processing algorithms of path information obtained by vision system are designed to provide accurate path information for intelligent vehicle navigation control.
(2) A vehicle dynamic model is established for simulation research on lateral and longitudinal control system
In view of the characteristic features of lateral motion control and longitudinal motion control of intelligent vehicle and the structure of the prototype vehicle DUTIV-I, a15degree-of-freedom nonlinear vehicle model which can accurately reflects the vehicle main movement states, the coupling characteristics of the tire model and the nonlinear characteristics of transmission system, is constructed by modularization method based on the basis data of the prototype vehicle. It is composed of engine model, hydraulic torque converter model, automatic transmission model,power train model, brake system model, wheel dynamics model, tire model, vehicle body model, suspension model and actuators model. The verification of established vehicle model is carried out by the simulation and experimental comparison under typical working conditions. The results show that the established vehicle model can accurately describe the vehicle dynamical characteristics and has the high simulation precision, which provides a reliable foundation for researching on lateral and longitudinal control.
(3) Study on lateral fuzzy control of intelligent vehicle using genetic algorithms.
A vision-based navigation of intelligent vehicle is taken as research object, and the lateral motion control model of vision-based navigation intelligent vehicle is established, which can describe the lateral motion behavior of intelligent vehicles. On this basis, the influence of look-ahead distance and speed on lateral control system is analyzed using root locus method, and the formula of look-ahead distance as a linear function of speed is established, which can optimize the path information data obtained by vision system. Aiming at highly nonlinear and parametric uncertain properties of non-holonomic intelligent vehicles, an automatic lateral controller consisting of a fuzzy feedback control law which can imitate human driving behavior and a feed-forward control law used to offset the disturbance of the curvature of reference paths is designed. In view of the membership functions and rules base designed either by experts knowledge, or iteratively by trial-and error are difficult to adaptive adjust according to the system characteristics and easy to produce overshoot or/and steady-state error. To confront this difficulty, the lateral fuzzy feedback control strategy of intelligent vehicles based on genetic algorithms is proposed. The membership functions and rules base of fuzzy feedback control law are automatically optimized and effectively determined by genetic algorithms. The stability of this fuzzy feedback control system is proved by Popov-Lyapunov. On this basis, the hierarchical control structure of fuzzy feedback control law according to driving speed, and in different layers fuzzy feedback controller is designed via genetic algorithms to optimize the parameters of membership functions and rules, which can effectively improve the adaptive ability lateral control system to the change of speed. The simulation results demonstrate the lateral control system proposed by this paper has better stable and dynamic performance.
(4) Study on longitudinal adaptive sliding-mode control of intelligent vehicle using via fuzzy-logic.
Firstly, the longitudinal nonlinear dynamic control model of intelligent vehicle is established which is used for the design of longitudinal control system. Secondly, the upper level controller of longitudinal control system is designed by classic PID control method. On the basis of upper level controller, aiming at the large parametric uncertain, external disturbance and time delay properties of longitudinal dynamic control model, the lower level controller of longitudinal control system is designed by adaptive fuzzy sliding mode control method, and the gain coefficients and boundary layers of sliding-mode control are adaptive tuned by fuzzy logic, which can effectively improve the performance and eliminate the chattering phenomenon, consequently, the vehicle speed can be controlled rapidly and exactly by proposed method. Then, the stability of the closed-loop longitudinal control system is proved by Lyapunov method. Finally, the switching criterion between the throttle actuator and brake actuator is presented, which can guarantee the smooth switching between throttle actuator and brake actuator. The simulation results demonstrate that the proposed control algorithm can guarantee the smooth transitions between throttle actuator and brake actuator and implement the rational and accurate control of longitudinal motion in presence of large model uncertainties and external distribution.
(5) Experimental study on lateral and longitudinal control algorithms proposed by this paper.
To further verify the effectiveness and accuracy of lateral and longitudinal control algorithms proposed this paper, aiming at the typical working conditions of lateral path tracking control and longitudinal speed tracking, the experimental tests of lateral motion control system and longitudinal motion control system of intelligent vehicle are carried out using the prototype vehicle DUTIV-I, respectively. The experimental results indicate that the lateral motion control system and longitudinal motion control system based on proposed method exhibit the better characteristics dynamic of dynamic response, tracking precision and robustness.
引文
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