• CN:11-2187/TH
  • ISSN:0577-6686

›› 2010, Vol. 46 ›› Issue (1): 68-73.

• 论文 • 上一篇    下一篇

球面3-RRR并联机构动力学建模与鲁棒-自适应迭代学习控制

王跃灵;金振林;李研彪   

  1. 燕山大学机械工程学院;燕山大学工业计算机控制工程河北省重点实验室
  • 发布日期:2010-01-05

Dynamic Modeling and Robust-adaptive Iterative Learning Control of 3-RRR Spherical Parallel Mechanism

WANG Yueling;JIN Zhenlin;LI Yanbiao   

  1. College of Mechanical Engineering, Yanshan University Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University
  • Published:2010-01-05

摘要: 采用Lagrange方法建立球面3-RRR并联机构基于动平台姿态参数的动力学模型。考虑在计算过程中采用线密度、厚度忽略等近似计算及工作过程中机构的磨损等微小变化造成的参数不确定性,进一步建立带参数不确定性的动力学模型。针对其在振动测试中的重复性动作等特点及参数不确定性设计鲁棒-自适应迭代学习控制器,并对此控制器进行稳定性证明。该控制器利用自适应算法对未知定常数的强大学习能力来补偿系统动力学模型的参数不确定项;利用迭代学习算法对期望轨迹进行零误差重复跟踪。由于该控制器吸取了系统动力学模型的已知信息,避免了一般模型未知系统迭代控制时必须满足的Lipschitz约束条件。仿真结果表明,在此控制器的作用下球面3-RRR并联机构能够很好地重复跟踪期望轨迹。

关键词: 迭代学习控制, 动力学, 拉格朗日方法, 球面3-RRR并联机构

Abstract: A dynamic model based on attitude parameters of moving platform for a 3-RRR spherical parallel robot is developed by using Lagrange method. Further, a dynamic model with parameter uncertainties is developed considering the parameters uncertainties caused by tiny changes such as approximately computing in linear density, thickness, and mechanism abrasion during working process. Aiming at its characteristic of acting repeatedly in vibration measuring and parameter uncertainties, a robust-adaptive iterative learning controller (ILC) is designed for this mechanism, and its stability is proved. The adaptive algorithm, which is powerful in leaning unknown constants, is used to compensate the uncertain parts of dynamics model, and the iterative learning algorithm is used to track the desired path without errors. Because of the utilization of certain information of dynamics model, the Lipschitz condition necessary for most unknown systems during iterative learning control is avoidable. The simulation results indicate that this robot can accurately track the desired path repeatedly.

Key words: 3-RRR spherical parallel robot, Dynamics, Iterative learning control, Lagrange method

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