模块化移动机械臂运动规划与控制
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摘要
移动机械臂作为一种更加新颖的机器人,在当今生产生活中发挥着越来越重要的作用,也引起了国内外研究学者的广泛关注。研究的热点主要集中在移动机械臂的运动规划与控制问题。多连杆模块化移动机械臂由于其结构的复杂性,数学模型的建立是极为困难的,所以控制方式不同于目前研究较多的两连杆平面移动机械臂系统。结构的复杂性,难免存在众多的不确定性,基于模型的集中式控制器设计方法受到了制约。因此,针对模块化移动机械臂的结构特点,本文在对目前的研究状况进行深入学习的基础上,提出了分层递阶分布式控制体系结构,将机器人规划控制层分为规划层和控制层,采取“运动学层面的协调规划,动力学层面的分布式控制”的规划控制策略,采取“协调规划,分而治之”的思想对平台子系统和机械臂子系统的动力学控制问题进行了研究,提出了相应的控制方法。
本文研究工作可以总结为以下几个方面：
(1)提出模块化移动机械臂系统的分层递阶分布式控制体系结构。由于模块化移动机械臂系统结构上的分布性,基于模型的集中式控制器设计几乎是不可实现的,被控对象的结构特征往往会制约控制器的体系结构设计。因此,在对多连杆模块化移动机械臂运动学和动力学同两连杆平面移动机械臂充分比较的基础上,提出模块化移动机械臂控制体系结构的性能要求,并且设计分层递阶分布式控制体系结构。将体系结构中的规划控制层分为运动学和动力学两个层面,运动规划从运动学层面考虑,动力学层面采用分布式控制方式,将移动平台和机械臂视为整个系统的两个子系统,将机械臂各关节进行线性解耦,体系结构的适应性分析说明了所提方案的可行性。
(2)基于所设计的控制体系结构,研究冗余模块化移动机械臂的运动规划问题,提出一种移动机械臂系统逆运动学求解的解析方法。针对由美国Pioneer公司生产的Powerbot移动平台和德国Amtec公司的PowerCube模块化机械臂所集成的移动机械臂系统,对其正向运动学进行了深入地分析。面向抓取空间小球这一具体任务,提出一种逆运动学求解的解析方法。对于不满足Pieper定理的机械臂系统,对部分关节加以约束,将有效地解决任务规划问题。
(3)针对移动机械臂动力学控制问题,首先研究平台子系统的轨迹跟踪控制问题。移动平台属于非完整系统,非完整系统模型建立相对容易,然而难免存在一定的建模误差和受到外界噪声干扰等不确定性影响。基于RBF神经网络对非线性函数的逼近能力,采用滑模控制方式设计了鲁棒自适应控制器。根据系统标称模型设计了等效控制律,通过滑模控制的鲁棒切换项来抑制系统不确定部分。利用神经网络自适应逼近鲁棒切换项的增益,并通过Lyapunov稳定性定理得到了神经网络权值自适应调节律。
(4)机械臂子系统的动力学控制问题,首先研究了较为简单的两连杆平面电驱动机械臂控制问题。考虑了关节的驱动电机特性,将机械臂系统视为由关节子系统和电机子系统组成的级联系统。根据关节角位移信息,通过Luenberger观测器对机械臂角速度进行观测。考虑了电机的动力学不确定性,利用Backstepping设计方法和滑模控制思想设计了系统的控制器。本章意在指出模型相对简单的被控对象,基于模型的控制器设计具有一定意义。
(5)提出基于ESO的SISO系统轨迹跟踪控制器设计方案,并将其应用到模块化机械臂PowerCube的关节空间分布式鲁棒控制问题。利用滑模控制的系统降阶特性,可以将状态变量数目为n的高阶系统降阶为状态变量为滑模面的一阶广义系统。将系统可能出现的建模不确定性和外界干扰视为系统“总的不确定性”,利用二阶ESO对之进行实时地估计和补偿,基于滑模控制设计思想设计了鲁棒控制器。将多模块机械臂各关节进行解耦线性化,根据所提出的控制器设计方法进行了PowerCube机械臂关节空间控制器设计。
(6)基于关节解耦线性化模型和ADRC理论体系框架,考虑参考输入信号受噪声影响情况下,设计了离散的PowerCube模块化机械臂关节空间分布式鲁棒控制器。鉴于(4)所设计的Luenberger观测器仅适用于精确模型的机械臂的不足,本部分进一步考虑了机械臂关节子系统和电机子系统均存在不确定性条件下的机械臂关节空间轨迹跟踪问题。依据Backstepping思想,对系统控制器进行了设计,所提控制策略在两自由度模块俯仰运动方向进行了验证。
Mobile manipulators have played a more and more important role in the civilian and military use in modern society, such as industry and human living area, and received tremendous attention. The hot spot of their research mainly concentrated on the motion planning and control of mobile manipulators. Since it is difficult to build the mathematical models for multi-link modular mobile manipulators because of the structure complexity, the control strategy derived from the widely researched two-link plane mobile manipulators is no longer valid. It is inevitable that numerous uncertainties exist because of the structure complexity. Therefore, the model-based centralized controller design method doesn't work. Based on the structure characters of modular mobile manipulators, and inquired into the current research situation, the hierarchical structure with decentralized control method is proposed in this dissertation. The planning and control layer is separated into the motion layer and dynamic control layer, the planning and control strategy of'coordinate planning of the kinematics layer, decentralized control of the dynamics layer' is taken. The dynamic control for the mobile robot subsystem and the manipulator subsystem are investigated with the idea of'coordinated planning, divide and rule', and the corresponding control strategy is brought forward.
The research work of this dissertation can be summarized as follows:
(1) The hierarchical structure with decentralized control of modular mobile manipulators will be brought forward. For the decentralized structure of modular mobile manipulators, it is almost impossible to design the centralized controller based on mathematical model, and the controller structure can be restricted by the plant structure character. Then, based on the fully comparison of kinematics and dynamics of the two-link manipulators with multi-link modular manipulators, the control system performance requirement of modular mobile manipulators will be put forward, and the hierarchical structure with decentralized control will be designed. The planning and control layer of the control system is divided into two layers including kinematics and dynamics. Motion planning can be settled from kinematics aspect, whereas the decentralized control is taken in the dynamic level. The mobile robot and robot manipulator are viewed as two subsystems of the mobile manipulator, and each joint of the modular manipulator can be linear decoupled from each other. Adaptability analysis of the control system structure is given to show the feasibility of the proposed strategy.
(2) Based on the designed control system structure, the motion planning problems of redundant mobile manipulators will be investigated. Analytic method for the inverse kinematics solution of mobile manipulators will be proposed. The forward kinematics of the mobile manipulator is deeply analyzed. In face of the certain task of snatching at small balls, an analytic method of inverse kinematics solving will be brought forward. For the manipulator subsystem doesn't satisfy Pieper theory, and some joint will be restricted by certain condition, the task planning problem will be settled.
(3) Considering the dynamic control problem, firstly, the trajectory tracking control of mobile robot subsystem will be investigated. Mobile robot belongs to nonholonomic system, and it is relative easy to build mathematical model for this system, whereas there must be some uncertainties such as the modeling errors and the external noise. Based on the approximation ability of RBF neural networks for nonlinear functions, we use the sliding mode control to design the robust adaptive controller. The nominal model of mobile robot is used to design the equivalent controller, and the robust switch item is used to tackle the uncertainty of the system. RBF neural network can be used to adaptively approximate the gains of the robust switch item, and the adaptive tuning laws can be derived via Lyapunov stability theorem.
(4) For the dynamic control of robot manipulator subsystem, we first consider the simple two-link planar electrically-driven robot manipulator. Taking the motor character of each joint into consideration, the robot manipulator can be viewed as the cascade system connected by the joint subsystem and the motor subsystem. Based on the measured joint displacement, the joint velocity can be estimated via Luenberger observer. The uncertainty of the motor dynamics is considered, and the controller will be designed by backstepping design method and sliding mode control. This work shows the controller design methodology for simple plant based on mathematical model.
(5) Trajectory tracking controller based on ESO for a class of SISO system will be proposed with application to joint space decentralized robust control for PowerCube modular manipulators. With the model reduction of sliding mode control, any nth-order higher system can be reduced into a generalized first-order system with its argument of sliding mode. The modeling uncertainty and the external disturbances are lumped into a'total uncertainty' which can be estimated and compensated in real time via second-order ESO, and the robust controller will be designed by sliding mode control method. The multi-link modular manipulator is decoupled. Based on the proposed control method, joint space controller design for PowerCube modular manipulator will be designed.
(6) The discrete decentralized robust controller for PowerCube joint space will be designed based on the linear decoupled model of the joints and the ADRC theory, considering the reference input is affected by noise. In (4), there is a deficiency that the Luenberger observer is just applicable to accurate model of robot manipulator. In this part, we consider the joint space trajectory tracking problem in the situation that both the uncertainties appeared in joint subsystem and motor subsystem. The controller is designed based on backstepping design strategy. The validation of the proposed control strategy is carried out in the pitching direction of a 2-DOF modular manipulator.
本文研究工作可以总结为以下几个方面：
(1)提出模块化移动机械臂系统的分层递阶分布式控制体系结构。由于模块化移动机械臂系统结构上的分布性,基于模型的集中式控制器设计几乎是不可实现的,被控对象的结构特征往往会制约控制器的体系结构设计。因此,在对多连杆模块化移动机械臂运动学和动力学同两连杆平面移动机械臂充分比较的基础上,提出模块化移动机械臂控制体系结构的性能要求,并且设计分层递阶分布式控制体系结构。将体系结构中的规划控制层分为运动学和动力学两个层面,运动规划从运动学层面考虑,动力学层面采用分布式控制方式,将移动平台和机械臂视为整个系统的两个子系统,将机械臂各关节进行线性解耦,体系结构的适应性分析说明了所提方案的可行性。
(2)基于所设计的控制体系结构,研究冗余模块化移动机械臂的运动规划问题,提出一种移动机械臂系统逆运动学求解的解析方法。针对由美国Pioneer公司生产的Powerbot移动平台和德国Amtec公司的PowerCube模块化机械臂所集成的移动机械臂系统,对其正向运动学进行了深入地分析。面向抓取空间小球这一具体任务,提出一种逆运动学求解的解析方法。对于不满足Pieper定理的机械臂系统,对部分关节加以约束,将有效地解决任务规划问题。
(3)针对移动机械臂动力学控制问题,首先研究平台子系统的轨迹跟踪控制问题。移动平台属于非完整系统,非完整系统模型建立相对容易,然而难免存在一定的建模误差和受到外界噪声干扰等不确定性影响。基于RBF神经网络对非线性函数的逼近能力,采用滑模控制方式设计了鲁棒自适应控制器。根据系统标称模型设计了等效控制律,通过滑模控制的鲁棒切换项来抑制系统不确定部分。利用神经网络自适应逼近鲁棒切换项的增益,并通过Lyapunov稳定性定理得到了神经网络权值自适应调节律。
(4)机械臂子系统的动力学控制问题,首先研究了较为简单的两连杆平面电驱动机械臂控制问题。考虑了关节的驱动电机特性,将机械臂系统视为由关节子系统和电机子系统组成的级联系统。根据关节角位移信息,通过Luenberger观测器对机械臂角速度进行观测。考虑了电机的动力学不确定性,利用Backstepping设计方法和滑模控制思想设计了系统的控制器。本章意在指出模型相对简单的被控对象,基于模型的控制器设计具有一定意义。
(5)提出基于ESO的SISO系统轨迹跟踪控制器设计方案,并将其应用到模块化机械臂PowerCube的关节空间分布式鲁棒控制问题。利用滑模控制的系统降阶特性,可以将状态变量数目为n的高阶系统降阶为状态变量为滑模面的一阶广义系统。将系统可能出现的建模不确定性和外界干扰视为系统“总的不确定性”,利用二阶ESO对之进行实时地估计和补偿,基于滑模控制设计思想设计了鲁棒控制器。将多模块机械臂各关节进行解耦线性化,根据所提出的控制器设计方法进行了PowerCube机械臂关节空间控制器设计。
(6)基于关节解耦线性化模型和ADRC理论体系框架,考虑参考输入信号受噪声影响情况下,设计了离散的PowerCube模块化机械臂关节空间分布式鲁棒控制器。鉴于(4)所设计的Luenberger观测器仅适用于精确模型的机械臂的不足,本部分进一步考虑了机械臂关节子系统和电机子系统均存在不确定性条件下的机械臂关节空间轨迹跟踪问题。依据Backstepping思想,对系统控制器进行了设计,所提控制策略在两自由度模块俯仰运动方向进行了验证。
Mobile manipulators have played a more and more important role in the civilian and military use in modern society, such as industry and human living area, and received tremendous attention. The hot spot of their research mainly concentrated on the motion planning and control of mobile manipulators. Since it is difficult to build the mathematical models for multi-link modular mobile manipulators because of the structure complexity, the control strategy derived from the widely researched two-link plane mobile manipulators is no longer valid. It is inevitable that numerous uncertainties exist because of the structure complexity. Therefore, the model-based centralized controller design method doesn't work. Based on the structure characters of modular mobile manipulators, and inquired into the current research situation, the hierarchical structure with decentralized control method is proposed in this dissertation. The planning and control layer is separated into the motion layer and dynamic control layer, the planning and control strategy of'coordinate planning of the kinematics layer, decentralized control of the dynamics layer' is taken. The dynamic control for the mobile robot subsystem and the manipulator subsystem are investigated with the idea of'coordinated planning, divide and rule', and the corresponding control strategy is brought forward.
The research work of this dissertation can be summarized as follows:
(1) The hierarchical structure with decentralized control of modular mobile manipulators will be brought forward. For the decentralized structure of modular mobile manipulators, it is almost impossible to design the centralized controller based on mathematical model, and the controller structure can be restricted by the plant structure character. Then, based on the fully comparison of kinematics and dynamics of the two-link manipulators with multi-link modular manipulators, the control system performance requirement of modular mobile manipulators will be put forward, and the hierarchical structure with decentralized control will be designed. The planning and control layer of the control system is divided into two layers including kinematics and dynamics. Motion planning can be settled from kinematics aspect, whereas the decentralized control is taken in the dynamic level. The mobile robot and robot manipulator are viewed as two subsystems of the mobile manipulator, and each joint of the modular manipulator can be linear decoupled from each other. Adaptability analysis of the control system structure is given to show the feasibility of the proposed strategy.
(2) Based on the designed control system structure, the motion planning problems of redundant mobile manipulators will be investigated. Analytic method for the inverse kinematics solution of mobile manipulators will be proposed. The forward kinematics of the mobile manipulator is deeply analyzed. In face of the certain task of snatching at small balls, an analytic method of inverse kinematics solving will be brought forward. For the manipulator subsystem doesn't satisfy Pieper theory, and some joint will be restricted by certain condition, the task planning problem will be settled.
(3) Considering the dynamic control problem, firstly, the trajectory tracking control of mobile robot subsystem will be investigated. Mobile robot belongs to nonholonomic system, and it is relative easy to build mathematical model for this system, whereas there must be some uncertainties such as the modeling errors and the external noise. Based on the approximation ability of RBF neural networks for nonlinear functions, we use the sliding mode control to design the robust adaptive controller. The nominal model of mobile robot is used to design the equivalent controller, and the robust switch item is used to tackle the uncertainty of the system. RBF neural network can be used to adaptively approximate the gains of the robust switch item, and the adaptive tuning laws can be derived via Lyapunov stability theorem.
(4) For the dynamic control of robot manipulator subsystem, we first consider the simple two-link planar electrically-driven robot manipulator. Taking the motor character of each joint into consideration, the robot manipulator can be viewed as the cascade system connected by the joint subsystem and the motor subsystem. Based on the measured joint displacement, the joint velocity can be estimated via Luenberger observer. The uncertainty of the motor dynamics is considered, and the controller will be designed by backstepping design method and sliding mode control. This work shows the controller design methodology for simple plant based on mathematical model.
(5) Trajectory tracking controller based on ESO for a class of SISO system will be proposed with application to joint space decentralized robust control for PowerCube modular manipulators. With the model reduction of sliding mode control, any nth-order higher system can be reduced into a generalized first-order system with its argument of sliding mode. The modeling uncertainty and the external disturbances are lumped into a'total uncertainty' which can be estimated and compensated in real time via second-order ESO, and the robust controller will be designed by sliding mode control method. The multi-link modular manipulator is decoupled. Based on the proposed control method, joint space controller design for PowerCube modular manipulator will be designed.
(6) The discrete decentralized robust controller for PowerCube joint space will be designed based on the linear decoupled model of the joints and the ADRC theory, considering the reference input is affected by noise. In (4), there is a deficiency that the Luenberger observer is just applicable to accurate model of robot manipulator. In this part, we consider the joint space trajectory tracking problem in the situation that both the uncertainties appeared in joint subsystem and motor subsystem. The controller is designed based on backstepping design strategy. The validation of the proposed control strategy is carried out in the pitching direction of a 2-DOF modular manipulator.
引文
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