不确定机器人控制系统中一类控制器设计方法研究_自动化专业论文
发布时间：2014-05-21
来源：人大经济论坛
自动化专业论文范文
目 录
摘 要I
AbstractII
第1章 绪论1
1.1 研究背景及意义1
1.1.1机器人发展简史与自动控制2
1.1.2本课题研究的意义3
1.2 机器人鲁棒控制方法概述4
1.2.1鲁棒控制方法4
1.2.2反馈线性化控制5
1.2.3变结构控制方法5
1.3 本章小结6
第2章 预备知识7
2.1 数学知识7
2.2 仿真知识9
2.2.1仿真的概念9
2.2.2 MATLAB语言简介10
2.3 机器人的数学模型10
2.4 机器人仿真模型的建立12
2.5 本章小结14
第3章 机器人的位姿描述15
3.1 位姿描述15
3.1.1平行移动15
3.1.2旋转17
3.1.3齐次变换18
3.2 运动学正向问题19
3.3 运动学逆向问题21
3.4 雅可比矩阵23
3.5 本章小结27
第四章 机器人的动力学模型28
4.1 利用牛顿定律建立机器人动力学模型28
4.1.1机器人平动的动力学模型28
4.1.2机器人转动的动力学模型30
4.2 利用拉格朗日方程建立机器人动力学模型31
4.3 二自由度机器人的动力学模型32
4.4 本章小结36
第五章 应用计算机力矩结构的控制策略37
5.1 计算力矩的基本思想37
5.2 仅对标称模型的计算力矩控制38
5.2.1仿真结果38
5.3 计算力矩的补偿控制结构39
5.3.1不确定性约束参数已知41
5.3.2不确定性约束参数未知45
5.4 本章小结47
结论48
参考文献49
致谢51
附录152
附录256
附录361
附录470
摘 要
机器人的控制问题无论在理论界还是工程界多年来一直备受人们关注。众所周知，机器人是一个十分复杂的多输入多输出非线性系统，它具有时变、强耦和非线性的动力学特点，对其进行控制也是十分复杂的，我们必须面对机器人大量不确定性因素的存在。在机器人的各种控制算法中，基予模型的计算力矩控制方法是十分有效的，其操作性也是很强的。然而，这种控制算法必须面临两大难题，第一，必须实现对机器人动力学模型的快速计算；第二，必须事先精确计算机器人的动力学模型，因为计算力矩算法在模型未知的情况下鲁棒性较差。但在实际中，即使获得一个较为理想的机器人动力学模型也是很困难的，何况在操作过程中机器人动力学模型的各个参数可能发生变化，同时还受到环境干扰和负载变化等许多不确定性因素的影响。
本论文以具有完整动力学模型的机器人系统，即不确定性机器人系统为研究对象，在现有的文献基础上，重点探讨基于计算力矩算法的补偿控制策略。
本文首先介绍了机器人的发展概论和机器人控制理论概况，然后对计算力矩控制算法的基本思想和主要特点作详细的阐述，紧接着探讨了基于计算力矩结构的不确定机器人的补偿控制算法。基本思想都是将不确定性机器人系统分解成标称系统和不确定系统：对于标称系统，采用计算力矩控制；对于不确定系统，采用机器人系统的回归矩阵或集中不确定性上界的包络函数，设计不同的补偿控制器，补偿控制器的输入与机器人的输入相叠加作为整个机器人系统的输入，使得机器人闭环系统能够实现全局一致最后有界、渐进稳定和指数稳定。并对二自由度串联机器人进行了MATLAB仿真，利用S-Function编写程序，证明了其有效性和可行性。
关键词 不确定机器人；计算力矩控制；鲁棒控制；机器人动力学；MATLAB/imulion；S-Function函数
Abstract
The control problems of robotic manipulators have received great attention in theoretical research and engineering for many years. It is well known that the robotic manipulator is a very complicated MIMO nonlinear system with time-varying strong-coupling and nonlinear dynamic characteristics, so the control for such a system is quite difficult, we have to face a lot of uncertainties. The model-based scheme popularly known as Computed Torque Control (CTC) is effective and its performance is excellent in various control strategies for robotic manipulator. However, the requirements for successfully implementing CTC are fast computation and perfect knowledge of dynamic model.. because CTC is not robust enough in uncertain model. In practice, unfortunately, it is impossible to obtain a prefect, or even reasonably accurate dynamic model of a robotic manipulator. Furthermore, the parameters of dynamics model of robotic manipulators may also be subject to change when the manipulator goes about its task. Meanwhile, the system can be influenced by uncertainties such as external disturbance and payload change.
In this dissertation, the system of robotic manipulator with entire dynamic model namely, the robotic system with uncertainties is regarded as controlled plant and the various compensation schemes based CTC are developed on base of references available.
The dissertation gives a brief description about the developing situation and control theory of robot firstly, and then the underlying idea and characteristic of CTC are introduced in detail. Subsequently discussed control strategies with compensation control structure which are based on CTC are proposed. The overall idea is that the system of robotic manipulators is decomposed as two parts: one is nominal system with perfect knowledge of dynamic model and the other is system with uncertainties. CTC is used to control nominal system. For uncertainties system, we utilize the regress of robotic system or bounding function on uncertainties to design different compensation controllers. The inputs of the two parts control the robotic systems together. These proposed control algorithms ensure Global Universe Ultimate Boundness Stability, Global Asymptotic Stability and Global Exponential Stability of the whole robotic system. The simulation results are presented for the same 2-DOF serial robotic manipulator in MATLAB, Use s-Function compiled programs, which validate the effectiveness and feasibility of the proposed schemes.
Keywords robotic manipulators with uncertainties; computed torque control; robust adaptive control; Robotic kinematics; MATLAB/ Simulation; S-Function
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