自动引导车运动分段控制技术研究
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摘要
非完整约束是指一种用不可积分的微分方程来表示的约束,它的存在使得系统更难控制；另一方面,非完整约束又广泛存在于自动引导车(AGV-Automatic Guided Vehicle)、太空机器人、欠驱动的水下舰船系统等实际系统中,在军事、工业、民用、深海太空业等领域具有很强的应用背景。AGV作为一种典型的非完整系统,研究其控制问题具有重要的理论意义和良好的实用价值。本文在对相关研究现状进行分析的基础上,对三轮AGV运动控制问题进行了深入研究,内容主要包括基于神经动力学模型的初期跟踪控制、结合能量优化策略的中段跟踪控制、镇定和跟踪统一的后期运动控制以及AGV整体运动分段控制(IMSC-Integrated Motion Sectionalized Control)等。
在系统调研AGV国内外研究现状的基础上,对AGV的结构及分类以及路径跟随、轨迹跟踪、点镇定等三种基本运动的若干关键运动控制技术进行了分析和梳理,指出了目前存在的问题及需要进一步研究的内容。
引入微分几何和非线性控制理论的一些基本概念和定理,给出了非完整系统以及约束的相关知识,综合归纳出了一套用于分析非完整系统的数学工具。用该工具分析了AGV的非完整性,并以三轮AGV为例,建立了AGV的运动学和动力学模型。
根据AGV系统的硬件组成和软件设计原理,对AGV控制系统进行合理的功能分配和模块划分,提出了所研究AGV的控制体系结构。
针对当AGV存在初始位姿误差或AGV的参考轨迹不连续时传统轨迹跟踪控制器会产生一个较大初始速度跳变的问题,在建立AGV跟踪误差系统模型的基础上,引入神经动力学思想,提出了一种基于生物激励神经动力学模型的AGV轨迹跟踪控制器。该控制器首先由运动学控制器产生一个理想控制律,接着利用神经动力学解决初始速度跳变问题,最后用快速终端滑模控制器进一步提高跟踪精度。仿真结果表明,该控制器能很好地解决跟踪过程中出现的初始速度跳变问题,最终实现系统全局渐进稳定。
针对AGV在运动中段能耗大的问题,在深入分析系统能耗情况的基础上,建立了AGV的能耗模型及运动学模型,提出了一种基于能量优化策略的AGV轨迹跟踪控制算法。能量优化控制器是上述控制算法的核心,设定电机能量效率函数为其目标函数,电机电枢等效电路电压平衡方程式和转矩方程式组成的方程组为其系统状态方程,使AGV能准确跟踪参考轨迹的条件为其状态约束,再加上一个控制输入约束,组成优化问题的三个约束,最后用遗传算法解优化问题得出一个最优速度控制律。仿真结果表明,该控制策略可以在使AGV顺利实现轨迹跟踪的同时达到能量优化的效果。
针对AGV系统的后期轨迹跟踪和镇定问题,在深入分析预测控制机理和稳定性的基础上,认真研究了存在非完整约束和控制输入约束的AGV轨迹跟踪的非线性模型预测控制问题,设计了基于模型预测终端控制器的终端镇定控制算法,并结合AGV的运动模型,使用状态观测器对含噪声的动态系统进行状态估计,进一步增强了轨迹跟踪控制的效果。同时,考虑到AGV需有避障功能,为镇定控制器设计了一个避障控制模块。计算机仿真结果证实了所设计轨迹跟踪算法的正确性和有效性。
为了实现对AGV整个运动过程的高精度和低能耗控制,本文提出了一种整体运动分段控制方法,即对AGV运动的初、中和后期三个不同阶段的运动,根据其各自特点分别采用上述三种控制方法,且将三种方法有机地组合成一种方法,并通过计算和仿真确定了各种方法所使用的运动区间,从而能更好地实现AGV在整个轨迹跟踪过程中的平滑性、节能性、鲁棒性及全局稳定性。
Non-holonomic constraint is characterized by non-integrable differential equations, making the system more difficult to control. On the other hand, non-holonomic constraint exists widely in practical systems, such as automatic guided vehicle (AGV), space robot, and under-actuated underwater ship system and so on, with a great potential for applications in military, industry, civil-application, deep-sea, outer-space and other fields. Since AGV is a typical non-holonomic system, it is of great theoretical and practical importance to study its control issue. Based on the current research status of the AGV, the present work aims to perform in-depth investigation of the motion control problem for tricycle AGV. Specifically, the main tasks of this study include neural dynamic model-based early stage tracking control, energy-efficient strategy-based middle stage tracking control, and last stage tracking and stabilization unified control, as well as the integrated motion sectionalized control (IMSC) in the entire AGV motion process.
Based on the systematic research of the current situation around the world, the dissertation systematically analyzes the AGV's structure and category, and several key motion control technologies for AGV's three basic motion forms (path following, trajectory tracking and point stabilization). Finally, several problems needing further investigation are pointed out.
By introducing some basic concepts and theorems of differential geometry and non-linear control theories, related knowledge for the non-holonomic system and non-holonomic constraint is given. Besides, a set of math tools for non-holonomic system are generalized, which are used for analyzing the AGV's non-holonomic properties. Taking tricycle AGV as an example, the kinematic and dynamic models for the AGV are developed.
According to the hardware structure and software design principle of the AGV system, a reasonable function module partition is proposed and the control architecture of AGV is presented.
In view of the initial velocity jump of the traditional tracking controller upon AGV's initial posture error or discontiguous reference trajectory, on the basis of tracking error model, a neural dynamic ideology is introduced and a trajectory tracking controller for AGV, which is based on the biologically inspired neural dynamics model, is proposed. First of all, the proposed tracking controller generates an ideal velocity law using the kinematic controller. Then, the problem of initial velocity jump is solved using the neural dynamics ideology. Finally, the tracking accuracy is further improved using a fast terminal sliding mode controller. Simulation results show that the tracking controller solves the initial velocity jump problem successfully and guarantes the global asymptotic stability of the system.
With consideration of the AGV's excessive energy consumption in motion, in light of the thorough analysis of the energy consumption status for the AGV system, an energy consumption model and a kinematic model are developed and a trajectory tracking controller basing on the energy-efficiency strategy is proposed. The core of the proposed controller is an energy optimal control module, which is with the motor energy efficiency function as the objective function. The constraint equations for this module are as follow:the voltage balance equation of the motor armature equivalent circuit and the torque equation are the system state equations, the condition equation of tracking the reference trajectory is the state constraint equation, and a control constraint equation. Finally, the above optimal problem is solved by genetic algorithm and an optimal velocity law is obtained. Simulation results show that the proposed controller achieves trajectory tracking and optimizes the energy consumption.
In order to resolve the AGV's tracking and stabilization unified control problem in the final stage, the non-linear model predictive control (NMPC) problem with non-holonomic constraints and control constraints is investigated based on the thorough analysis of the principle and stability of the predictive control method. Also a terminal stability control algorithm based on model predictive terminal control is proposed. In order to improve the tracking accuracy, a state observer is designed to estimate the AGV's state in the dynamic system. Besides, considering the fact that the AGV should have obstacle-avoidance function, an obstacle-avoidance module is designed. The validity of the controller is validated by computer simulation.
In order to achieve control with high precision and low energy consumption for the entire AGV tracking process, a method called integrated motion sectionalized control (IMSC) is proposed. In this method, the three forementioned control methods are adopted for the early, middle and last state motion of AGV respectively, according to their characteristics. Through combining the three control methods into one and determining the applicable motion interval for each method by calculation and simulation, IMSC is capable of providing smoothness, energy saving, robustness and global stability for the AGV system.
在系统调研AGV国内外研究现状的基础上,对AGV的结构及分类以及路径跟随、轨迹跟踪、点镇定等三种基本运动的若干关键运动控制技术进行了分析和梳理,指出了目前存在的问题及需要进一步研究的内容。
引入微分几何和非线性控制理论的一些基本概念和定理,给出了非完整系统以及约束的相关知识,综合归纳出了一套用于分析非完整系统的数学工具。用该工具分析了AGV的非完整性,并以三轮AGV为例,建立了AGV的运动学和动力学模型。
根据AGV系统的硬件组成和软件设计原理,对AGV控制系统进行合理的功能分配和模块划分,提出了所研究AGV的控制体系结构。
针对当AGV存在初始位姿误差或AGV的参考轨迹不连续时传统轨迹跟踪控制器会产生一个较大初始速度跳变的问题,在建立AGV跟踪误差系统模型的基础上,引入神经动力学思想,提出了一种基于生物激励神经动力学模型的AGV轨迹跟踪控制器。该控制器首先由运动学控制器产生一个理想控制律,接着利用神经动力学解决初始速度跳变问题,最后用快速终端滑模控制器进一步提高跟踪精度。仿真结果表明,该控制器能很好地解决跟踪过程中出现的初始速度跳变问题,最终实现系统全局渐进稳定。
针对AGV在运动中段能耗大的问题,在深入分析系统能耗情况的基础上,建立了AGV的能耗模型及运动学模型,提出了一种基于能量优化策略的AGV轨迹跟踪控制算法。能量优化控制器是上述控制算法的核心,设定电机能量效率函数为其目标函数,电机电枢等效电路电压平衡方程式和转矩方程式组成的方程组为其系统状态方程,使AGV能准确跟踪参考轨迹的条件为其状态约束,再加上一个控制输入约束,组成优化问题的三个约束,最后用遗传算法解优化问题得出一个最优速度控制律。仿真结果表明,该控制策略可以在使AGV顺利实现轨迹跟踪的同时达到能量优化的效果。
针对AGV系统的后期轨迹跟踪和镇定问题,在深入分析预测控制机理和稳定性的基础上,认真研究了存在非完整约束和控制输入约束的AGV轨迹跟踪的非线性模型预测控制问题,设计了基于模型预测终端控制器的终端镇定控制算法,并结合AGV的运动模型,使用状态观测器对含噪声的动态系统进行状态估计,进一步增强了轨迹跟踪控制的效果。同时,考虑到AGV需有避障功能,为镇定控制器设计了一个避障控制模块。计算机仿真结果证实了所设计轨迹跟踪算法的正确性和有效性。
为了实现对AGV整个运动过程的高精度和低能耗控制,本文提出了一种整体运动分段控制方法,即对AGV运动的初、中和后期三个不同阶段的运动,根据其各自特点分别采用上述三种控制方法,且将三种方法有机地组合成一种方法,并通过计算和仿真确定了各种方法所使用的运动区间,从而能更好地实现AGV在整个轨迹跟踪过程中的平滑性、节能性、鲁棒性及全局稳定性。
Non-holonomic constraint is characterized by non-integrable differential equations, making the system more difficult to control. On the other hand, non-holonomic constraint exists widely in practical systems, such as automatic guided vehicle (AGV), space robot, and under-actuated underwater ship system and so on, with a great potential for applications in military, industry, civil-application, deep-sea, outer-space and other fields. Since AGV is a typical non-holonomic system, it is of great theoretical and practical importance to study its control issue. Based on the current research status of the AGV, the present work aims to perform in-depth investigation of the motion control problem for tricycle AGV. Specifically, the main tasks of this study include neural dynamic model-based early stage tracking control, energy-efficient strategy-based middle stage tracking control, and last stage tracking and stabilization unified control, as well as the integrated motion sectionalized control (IMSC) in the entire AGV motion process.
Based on the systematic research of the current situation around the world, the dissertation systematically analyzes the AGV's structure and category, and several key motion control technologies for AGV's three basic motion forms (path following, trajectory tracking and point stabilization). Finally, several problems needing further investigation are pointed out.
By introducing some basic concepts and theorems of differential geometry and non-linear control theories, related knowledge for the non-holonomic system and non-holonomic constraint is given. Besides, a set of math tools for non-holonomic system are generalized, which are used for analyzing the AGV's non-holonomic properties. Taking tricycle AGV as an example, the kinematic and dynamic models for the AGV are developed.
According to the hardware structure and software design principle of the AGV system, a reasonable function module partition is proposed and the control architecture of AGV is presented.
In view of the initial velocity jump of the traditional tracking controller upon AGV's initial posture error or discontiguous reference trajectory, on the basis of tracking error model, a neural dynamic ideology is introduced and a trajectory tracking controller for AGV, which is based on the biologically inspired neural dynamics model, is proposed. First of all, the proposed tracking controller generates an ideal velocity law using the kinematic controller. Then, the problem of initial velocity jump is solved using the neural dynamics ideology. Finally, the tracking accuracy is further improved using a fast terminal sliding mode controller. Simulation results show that the tracking controller solves the initial velocity jump problem successfully and guarantes the global asymptotic stability of the system.
With consideration of the AGV's excessive energy consumption in motion, in light of the thorough analysis of the energy consumption status for the AGV system, an energy consumption model and a kinematic model are developed and a trajectory tracking controller basing on the energy-efficiency strategy is proposed. The core of the proposed controller is an energy optimal control module, which is with the motor energy efficiency function as the objective function. The constraint equations for this module are as follow:the voltage balance equation of the motor armature equivalent circuit and the torque equation are the system state equations, the condition equation of tracking the reference trajectory is the state constraint equation, and a control constraint equation. Finally, the above optimal problem is solved by genetic algorithm and an optimal velocity law is obtained. Simulation results show that the proposed controller achieves trajectory tracking and optimizes the energy consumption.
In order to resolve the AGV's tracking and stabilization unified control problem in the final stage, the non-linear model predictive control (NMPC) problem with non-holonomic constraints and control constraints is investigated based on the thorough analysis of the principle and stability of the predictive control method. Also a terminal stability control algorithm based on model predictive terminal control is proposed. In order to improve the tracking accuracy, a state observer is designed to estimate the AGV's state in the dynamic system. Besides, considering the fact that the AGV should have obstacle-avoidance function, an obstacle-avoidance module is designed. The validity of the controller is validated by computer simulation.
In order to achieve control with high precision and low energy consumption for the entire AGV tracking process, a method called integrated motion sectionalized control (IMSC) is proposed. In this method, the three forementioned control methods are adopted for the early, middle and last state motion of AGV respectively, according to their characteristics. Through combining the three control methods into one and determining the applicable motion interval for each method by calculation and simulation, IMSC is capable of providing smoothness, energy saving, robustness and global stability for the AGV system.
引文
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