基于动力补偿的液压并联运动平台控制策略

袁立鹏; 董彦良; 赵克定; 许宏光

基于动力补偿的液压并联运动平台控制策略

哈尔滨工业大学机电工程学院, 哈尔滨 150001

基金项目: 国家985工程资助项目

详细信息

作者简介:
袁立鹏(1976-), 男,黑龙江哈尔滨人,讲师,hitylp@126.com.

中图分类号: TP 212.12
计量
- 文章访问数: 3132
- HTML全文浏览量: 76
- PDF下载量: 901
- 被引次数: 0
出版历程
- 收稿日期: 2005-09-05
- 网络出版日期: 2006-08-31

Dynamic compensator control strategy of hydraulic parallel manipulator

School of Mechatronic Engineering, Harbin Institute of Technology, Harbin 150001, China

摘要

摘要: 针对所研制的飞行模拟器用大负载液压六自由度并联运动平台,在对系统闭环动力学模型进行详细分析的基础上,依据系统数学模型存在外扰力、摩擦力及不确知参数等因素影响的控制特性,利用系统闭环动力学模型的动力补偿特性,采用六维动力补偿器,提出一种PD控制器加小波神经网络补偿器进行在线动力学补偿的实时控制策略,并通过实验验证了其控制的有效性.结果表明:该方法具有良好的跟踪特性,能够提高系统响应快速性、运动精度及抗负载扰动能力,很大程度上克服了系统的动力耦合及参数时变和未知力扰动带来的影响,为多自由度运动系统的高性能实时控制开辟了崭新途径.
- 运动平台 /
- 小波 /
- 神经网络 /
- 动力学
Abstract: The prototype of a hydraulic driven 6-DOF parallel manipulator used in fly model simulator was presented. Considering on the mathematical model of the system which has many uncertain effect factors such as disturbed force, friction force and uncertain parameter, the research on the closed loop dynamics model of the system was made. According to the dynamic compensation characteristics of the closed loop model of the system, six-dimension dynamic compensator was adopted. A real time control strategy which is composed by PD controller and wavelet neural network was put forward to make dynamic compensation. Experiment result shows that this method has characteristics of well tracing ability. And it can improve the response time, movement accuracy and resistance to load disturbance of the system. It also can overcome the effects which are produced by the dynamic coupling, real time varied parameters and unknown disturbance. This method presents a brand-new path for high performance of the parallel manipulator mechanism.
- manipulator /
- wavelet /
- neural networks /
- dynamics

HTML全文

参考文献(1)

[1] 杨灏泉.飞行模拟器六自由度运动系统及其液压伺服系统的研究 .哈尔滨:哈尔滨工业大学机电工程学院,2002 Yang Haoquan. Research on 6-DOF motion system of flight simulation and its hydraulic servo system . Harbin:School of Mechatronic Engineering, Harbin Institute of Technology, 2002(in Chinese) [2] 黄茹楠,高英杰,王洪瑞.并联机器人的一种鲁棒最优控制结构[J]. 燕山大学学报, 1999, 23 (2):175-177 Huang Runan, Gao Yingjie, Wang Hongrui. A kind of robust optimum control of parallel robot[J]. Journal of Yanshan University, 1999, 23(2):175-177(in Chinese) [3] 黄真,孔令富,方跃法.并联机器人机构学理论及控制[M].北京:机械工业出版社,1997:303-350 Huan Zhen, Kong Lingfu, Fang Yuefa. Mechanism theory and control of parallel robot[M]. Beijing:Mechanism Industry Publishing Company, 1997:303-350(in Chinese) [4] Asada H, Slotine J J E. Robot analysis and control[J]. John Wiley and Sons, 1985, 16(3):61-65 [5] Ting Y, Chen Y S, Wang S M. Task-space control algorithm for Stewart platform IEEE/CSS. Proc IEEE Conf Decis Control. Piscataway, NJ, USA:IEEE, 1999:3857-3862 [6] Kim N I, Lee C W. High speed tracking control of Stewart platform manipulator via enhanced sliding mode control IEEE. Proc IEEE Int Conf Rob Autom. Piscataway, NJ, USA:IEEE, 1998:2716-2721 [7] Kang J Y, Kim D H, Lee K I. Robust tracking control of Stewart platform IEEE. Proc IEEE Conf Decis Control. Kobe, Jpn:IEEE, 1996:3014-3019 [8] Sirouspour M R, Salcudean S E. Nonlinear control of hydraulic robots . IEEE Transactions on Robotics and Automation, 2001, 17(2):173-182 [9] Lee S H, Song J B, Choi W C, et al. Position control of a Stewart platform using inverse dynamics control with approximate dynamics[J]. Mechatronics, 2003, 13(6):605-619 [10] Guo T Y, Qu D K, Xu F. AC motor control based on wavelet network IEEE Proc Int Conf Mach Learning Cybernetics. New York, 10016-5997, United States:IEEE, 2004:861-865 [11] Shashidhara H L, Lohani S, Gadre V M. Function learning using wavelet neural networks IEEE Industrial Electronics Society. Proc IEEE Int Conf Ind Technol. Piscataway, NJ, USA:IEEE, 2000:335-340 [12] Lewis F L, Yesilidrek A, Liu K. Multilayer neural net robot controller: structure and stability proofs[J]. IEEE Trans Neural Networks, 1996, 3(7):388-399 [13] Lin C K. Adaptive tracking controller design for robotic systems using Gaussian wavelet networks[J]. IEE Proc Control Theory Appl, 2002, 149(4):316-322 [14] Huang S N, Tan K K, Lee T H. Decentralized control design for large-scale systems with strong interconnections using neural networks[J]. IEEE Transactions on Automatic Control, 2003, 48(5):805-810 [15] Lewis F L. Neural network control of robot manipulators[J]. IEEE Expert, 1996,11(3):64-75 [16] Selmic R R, Lewis F L. Neural-network approximation of piecewise continuous functions:application to friction compensation[J]. IEEE Tran on Neural Netwroks, 2002, 3(13):745-751

施引文献

资源附件(0)

访问统计