受限柔性机器人装置、建模与控制的研究
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摘要
近年来受限柔性机器人理论与应用的研究受到广泛关注。它的研究对于扩大机器人在工业生产中的应用范围有着深刻意义。
论文首先在综述国内外柔性机器人研究的基础上,提出了研制受限柔性机器人装置的总体方案。遵循此方案,自行设计、加工、安装了一台受限柔性机器人实验装置。论文第三章重点阐述了该装置研制中机械臂的驱动与保护问题。在其他章节中也分别有侧重地阐述了该装置研制中的数据通信、信号检测与处理等关键技术。
论文第四章基于Lagrange乘子法和Hamilton原理推出了平面双柔性杆受限柔性机器人的动力学模型,并得出了与研制的机器人装置相应的模型。该动力学模型具有结构简明且与一般刚性机器人模型相似,简化了基于其他动力学模型研究受限柔性机器人控制问题的复杂性。
论文第五及第六章对受限柔性机器人的位置控制进行了研究。第五章提出并设计了一种神经网络直接自适应控制器。该控制器用神经网络担任辨识任务,控制受限柔性机器人在未知对象模型情况下,实现对柔性机械臂的精确定位。第六章结合PID控制、模糊控制、及专家知识提出并设计了一种专家PID控制器,该控制器不仅可以通过上位机通讯在线修改PID中的三个参数,使被控制对象有良好的动、稳态性能,而且计算时间短,易于用单片机实现。两种控制器在实验装置上的控制结果均获得较高的位置控制精度。论文第七及第八章研究了受限柔性机器人的位置/力混合控制。第七章首先对论文第四章推导的位置/力混合控制问题的动力学模型进行合理简化,作了计算机仿真。根据仿真结果控制研制的机器人按受限的轨迹运动,并使末端与接触面的受力保持恒定,控制精度较好。为了得到更高的位置/力混合控制精度,克服不确定性因素对机器人控制结果的影响,第八章利用神经网络的映射功能,将神经网络作为系统不确定性因素的一种补偿工具,提出了基于神经网络方法的机器人位置/力混合控制。对控制器进行了仿真,取得了满意的控制结果。
In recent years, more attention has been paid on the study of constrained flexible-link robot manipulators. The research has great significance to enlarge robot applications in industry.
After summarizing the study of constrained flexible-link robot manipulators, this dissertation proposes the program to manufacture the flexible-link robot manipulators. Based on this program, One constrained flexible-link robot manipulators is designed, machined and installed by author himself. Chapter 3, studies the drive and protection of the manufactured robot manipulators and in other chapter, studies the key techniques of manufacturing the flexible robot manipulators, such as data communication, signal testing and disposing.
Based on the D'Alembert-Lagrange principle, Chapter 4 discusses the dynamic modeling problem for a class of constrained planar two-link flexible manipulators, and establishes a set of dynamic equations which describe the motion of the robotic systems of our manufacturing robot. Compared with the present dynamic models for same robotic manipulator systems. the motion equations and vibration equations built up in the dissertation are more precise and simpler. Moreover, this dynamic model has a similar configuration with unconstrained rigid-link manipulators, which make it easy to control the complicated constrained flexible manipulators.
Chapter 5 and Chapter 6 deal with the position control of the constrained flexible robot. Chapter 5 proposes and designs a direct adaptive controller on neural network. This controller used the neural network to identify the model of flexible. Based on the PID control , Fuzzy control and the knowledge of expert, Chapter 6 proposes and designs an expert PID controller, the parameters of PID are modified on line by the upper computer. The result of control verified on the constrained flexible robot shows that these two kinds of controller can control the mechanical arm to position accurately. Chapter 7 and Chapter 8 deal with the position/force control of the constrained flexible robot. Chapter 7 simplifies the dynamic model established in Chapter 4 reasonably and computes the law of control by the computer. According the law of control, controls the flexible-link manipulators to move on the constrained trajectory and hold the force on the constrained surface to be constant. The result of control is good. In order to get better, Chapter 8 proposes a hybrid position/force control on neural network method. This controller used neural network to compensate the uncertain effect on the flexible robot. The simulation result of this controller shows that this method is effective.
论文首先在综述国内外柔性机器人研究的基础上,提出了研制受限柔性机器人装置的总体方案。遵循此方案,自行设计、加工、安装了一台受限柔性机器人实验装置。论文第三章重点阐述了该装置研制中机械臂的驱动与保护问题。在其他章节中也分别有侧重地阐述了该装置研制中的数据通信、信号检测与处理等关键技术。
论文第四章基于Lagrange乘子法和Hamilton原理推出了平面双柔性杆受限柔性机器人的动力学模型,并得出了与研制的机器人装置相应的模型。该动力学模型具有结构简明且与一般刚性机器人模型相似,简化了基于其他动力学模型研究受限柔性机器人控制问题的复杂性。
论文第五及第六章对受限柔性机器人的位置控制进行了研究。第五章提出并设计了一种神经网络直接自适应控制器。该控制器用神经网络担任辨识任务,控制受限柔性机器人在未知对象模型情况下,实现对柔性机械臂的精确定位。第六章结合PID控制、模糊控制、及专家知识提出并设计了一种专家PID控制器,该控制器不仅可以通过上位机通讯在线修改PID中的三个参数,使被控制对象有良好的动、稳态性能,而且计算时间短,易于用单片机实现。两种控制器在实验装置上的控制结果均获得较高的位置控制精度。论文第七及第八章研究了受限柔性机器人的位置/力混合控制。第七章首先对论文第四章推导的位置/力混合控制问题的动力学模型进行合理简化,作了计算机仿真。根据仿真结果控制研制的机器人按受限的轨迹运动,并使末端与接触面的受力保持恒定,控制精度较好。为了得到更高的位置/力混合控制精度,克服不确定性因素对机器人控制结果的影响,第八章利用神经网络的映射功能,将神经网络作为系统不确定性因素的一种补偿工具,提出了基于神经网络方法的机器人位置/力混合控制。对控制器进行了仿真,取得了满意的控制结果。
In recent years, more attention has been paid on the study of constrained flexible-link robot manipulators. The research has great significance to enlarge robot applications in industry.
After summarizing the study of constrained flexible-link robot manipulators, this dissertation proposes the program to manufacture the flexible-link robot manipulators. Based on this program, One constrained flexible-link robot manipulators is designed, machined and installed by author himself. Chapter 3, studies the drive and protection of the manufactured robot manipulators and in other chapter, studies the key techniques of manufacturing the flexible robot manipulators, such as data communication, signal testing and disposing.
Based on the D'Alembert-Lagrange principle, Chapter 4 discusses the dynamic modeling problem for a class of constrained planar two-link flexible manipulators, and establishes a set of dynamic equations which describe the motion of the robotic systems of our manufacturing robot. Compared with the present dynamic models for same robotic manipulator systems. the motion equations and vibration equations built up in the dissertation are more precise and simpler. Moreover, this dynamic model has a similar configuration with unconstrained rigid-link manipulators, which make it easy to control the complicated constrained flexible manipulators.
Chapter 5 and Chapter 6 deal with the position control of the constrained flexible robot. Chapter 5 proposes and designs a direct adaptive controller on neural network. This controller used the neural network to identify the model of flexible. Based on the PID control , Fuzzy control and the knowledge of expert, Chapter 6 proposes and designs an expert PID controller, the parameters of PID are modified on line by the upper computer. The result of control verified on the constrained flexible robot shows that these two kinds of controller can control the mechanical arm to position accurately. Chapter 7 and Chapter 8 deal with the position/force control of the constrained flexible robot. Chapter 7 simplifies the dynamic model established in Chapter 4 reasonably and computes the law of control by the computer. According the law of control, controls the flexible-link manipulators to move on the constrained trajectory and hold the force on the constrained surface to be constant. The result of control is good. In order to get better, Chapter 8 proposes a hybrid position/force control on neural network method. This controller used neural network to compensate the uncertain effect on the flexible robot. The simulation result of this controller shows that this method is effective.
引文
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