基于X-Y平台的力/位置智能控制研究
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摘要
利用X-Y平台进行抛光,打磨等作业时,平台与环境接触,在精确位置跟踪的同时需对接触表面施加一定的期望力。针对此种受限X-Y平台,本文在参考国内外相关文献的基础上,对其进行了力控制研究,主要研究内容如下:
首先,在X-Y平台的约束动力学方程基础上,提出了一种在不改变X-Y平台位置控制器的前提下实现力控制的方案,即系统的外环为力控制回路,根据力误差修正参考位置,使平台与环境的实际接触力跟踪期望力。在力控制回路,利用改进Elman网络在线辨识未知环境,不需要环境位置和刚度的先验知识,这种控制方法有误差补偿作用,对力控制中的干扰和环境不确定因素具有鲁棒性,仿真结果表明了控制方案的有效性。
其次,运用力/位置混合控制方法对X-Y平台进行力控制。在提出的力/位置混合控制策略中,位置控制回路采用PD控制,同时,将RBF神经网络用于X-Y定位平台的力控制回路中,利用RBF神经网络学习力控制中的不确定上界,并与反馈控制器结合,进一步确保了控制系统的稳定性,有效地提高了系统的精度和自适应能力。
最后,本文将阻抗控制应用于X-Y平台的力控制中,阻抗控制的关键在于阻抗参数的选择,根据接触力和平台位置、速度的变化对阻抗模型参数进行实时模糊调节,减少了受限运动中力干扰的影响,提高了全局力控制效果。
The end-effector of a X-Y positioning table is required to keep precise postion tracking and keep a contact force along the outward normal of the constraint surface in tasks such as deburring and grinding. Base on the relevant literature at home and abroad, we did some researches on force control for X-Y table. The main research of this paper is concluded as follows.
First, a kind of force control strategy for X-Y table is proposed on the premise that the position controller is not changed, namely, the outer loop is force control loop. The reference position is modified according to the force error so that the expected contact force between the X-Y table and the environment can be achieved. In force control loop, unknown environments is identificated by a modified Elman neural network online. The prior knowledge of the environment is not required. This method can compensate the errors of environment and guarantees the robustness of force control. Simulation results show that this control strategy is effective.
Second, a kind of hypbrid force/position controller based on RBF neural network is presented for constrained X-Y positioning table. PD controler is used in position control. In force control, the RBF neural network is applied to learning the upper bound of system’s uncertainties and proportion controller strengthens the completeness of this control strategy. The results of numerical simulation demonstrate the stability and robustness of the system. The precision and adaptability is improved effectively under the proposed control strategy.
Finally, an impedance control algorithm is presented in force control for X-Y positioning table. The key of this algorithm is to select the proper impedance parameters. So the parameters of impedance model are adjusted fuzzily real-time according to the change of force, positon and volocity of X-Y table. This method can reduce disturbance in constrained motion and can improve the global force control performance.
首先,在X-Y平台的约束动力学方程基础上,提出了一种在不改变X-Y平台位置控制器的前提下实现力控制的方案,即系统的外环为力控制回路,根据力误差修正参考位置,使平台与环境的实际接触力跟踪期望力。在力控制回路,利用改进Elman网络在线辨识未知环境,不需要环境位置和刚度的先验知识,这种控制方法有误差补偿作用,对力控制中的干扰和环境不确定因素具有鲁棒性,仿真结果表明了控制方案的有效性。
其次,运用力/位置混合控制方法对X-Y平台进行力控制。在提出的力/位置混合控制策略中,位置控制回路采用PD控制,同时,将RBF神经网络用于X-Y定位平台的力控制回路中,利用RBF神经网络学习力控制中的不确定上界,并与反馈控制器结合,进一步确保了控制系统的稳定性,有效地提高了系统的精度和自适应能力。
最后,本文将阻抗控制应用于X-Y平台的力控制中,阻抗控制的关键在于阻抗参数的选择,根据接触力和平台位置、速度的变化对阻抗模型参数进行实时模糊调节,减少了受限运动中力干扰的影响,提高了全局力控制效果。
The end-effector of a X-Y positioning table is required to keep precise postion tracking and keep a contact force along the outward normal of the constraint surface in tasks such as deburring and grinding. Base on the relevant literature at home and abroad, we did some researches on force control for X-Y table. The main research of this paper is concluded as follows.
First, a kind of force control strategy for X-Y table is proposed on the premise that the position controller is not changed, namely, the outer loop is force control loop. The reference position is modified according to the force error so that the expected contact force between the X-Y table and the environment can be achieved. In force control loop, unknown environments is identificated by a modified Elman neural network online. The prior knowledge of the environment is not required. This method can compensate the errors of environment and guarantees the robustness of force control. Simulation results show that this control strategy is effective.
Second, a kind of hypbrid force/position controller based on RBF neural network is presented for constrained X-Y positioning table. PD controler is used in position control. In force control, the RBF neural network is applied to learning the upper bound of system’s uncertainties and proportion controller strengthens the completeness of this control strategy. The results of numerical simulation demonstrate the stability and robustness of the system. The precision and adaptability is improved effectively under the proposed control strategy.
Finally, an impedance control algorithm is presented in force control for X-Y positioning table. The key of this algorithm is to select the proper impedance parameters. So the parameters of impedance model are adjusted fuzzily real-time according to the change of force, positon and volocity of X-Y table. This method can reduce disturbance in constrained motion and can improve the global force control performance.
引文
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2盛伯浩.数控机床的现状与发展.制造技术与机床, 2004, (1):20-23
3王中华,王兴松,徐卫良. X-Y定位平台的鲁棒自适应摩擦补偿.东南大学学报(自然科学版), 2002,32(1):69-72
4 Wang Chih-Lang, Hsieh King-Ching, Han Song-Yu. A trajectory tracking of piezo-driven x-y table system using fuzzy T-S model-based variable structure decentralized control. IEEE International Conference on Fuzzy Systems, Proceedings of the IEEE International Conference on Fuzzy Systems, FUZZ-IEEE, 2005, (2): 49-54.
5魏立新,于岩,王洪瑞.基于X-Y定位平台的力/位置混合控制.控制工程. 2006,13(s0): 54-56
6任守信.超精密加工中的机器人应用与研究.科技时代. 2008,3:3-5
7李杨,李劲松,崔艳华.数控化改造两坐标工作台.山东机械. 2001,3:45-48
8张涛,路长厚,袭著燕. XY交流伺服工作台摩擦实验方法的研究.仪器仪表学报. 2006,27(6):479-481
9宋文杰.超磁致伸缩二维精密工作台研究.机电工程. 2007,24(3):59-60
10周鸿斌,张建民,付红伟等. PRS-XY型混联数控机床工作台误差补偿技术.北京理工大学学报. 2007,27(2):120-124
11孙宏昌,张建民,贾东永等. PRS2XY型混联数控机床的三维刀补实验研究.现代制造工程. 2006,2:26-28
12李翠红,孟永钢,田煌等.基于超磁致伸缩和压电陶瓷的超精密定位工作台.纳米技术与精密工程. 2005,3(4):257-261
13王伟.智能控制的发展趋势与展望.精密制造与自动化. 2008,3:4-6
14丁群燕.智能控制的产生与发展.装备制造技术. 2008,11:125-126
15吴晓莉,林哲辉等. MATLAB辅助模糊系统设计.西安:西安电子科技大学出版, 2002:15-18
16 T.Takagi, M.Sugeno. Fuzzy Identification of Systems and Its Application toModeling and Control. IEEE Trans. on Systems, Man and Cybernetics, 1985, 15(1):116-132
17李士勇.模糊控制-神经网络控制和智能控制论.哈尔滨:哈尔滨工业大学出版社, 2002:431-447
18张恩勤,施颂椒,高卫华等.模糊控制系统近年来的研究与发展.控制理论与应用. 2001,18(1):7-11
19李少远,席裕庚,陈增强等.智能控制的新进展.控制与决策,2000.15(1):1-5
20王立新.模糊系统与模糊控制教程.北京:清经大学出版.2003:2-11
21李岩.神经网络的应用及其发展.商情. 2008,5:83-84
22徐丽娜.神经网络控制.哈尔滨:哈尔滨工业大学出版社,1999:30-42
23程云鹏.矩阵论.西安:西北工业大学出版社. 2000:109-126
24李换琴,万百五.训练前向神经网络的全局优化新算法及其应用.系统工程理论与实践.2003,8:42-47
25代颖.不确定性机器人鲁棒自适应控制方法研究. [西安交通大学工学博士学位论文]. 1998:25-55
26胡寿松.自动化控制原理.北京:科学出版社. 2001:501-508
27周建平,陈红军,王林山.一类变时滞神经网络的全局指数稳定性. 2007,24(3): 431- 434
28胡跃明.非线性控制系统理论与应用.国防工业出版社. 2002:57-59
29姜玉宪.控制系统仿真.北京:北京航空航天大学出版社. 1998:2-10
30孙亮. MATLAB语言与控制系统仿真.北京:北京工业大学出版社. 2001:122-123
31曾建军,李世航. MATLAB语言与数学建模.合肥:安徽大学出版社.2005:1-2
32通用线性模块和XY工作台.固高科技有限公司. 2009.
33王雪松,程玉虎,易建强等.基于Elman网络的非线性系统增强式学习控制.中国矿业大学学报. 2006,35(5): 653-658
34 MeralM, Sengor NS. System identification with hybrid Elman network. Signal Processing and Communications Applications Conference, 2004, Proceedings of the IEEE 12th 28-30 2004,4: 80-83
35 Yang XiYun, Xu DaPing, Han XiaoJuan. Predictive functional control with modifiedElman neural network for reheated steam temperature. 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005:4699-4703
36 Lin F.J, Teng L.T, Chu H. Modified Elman neural network controller with improved particle swarm optimisation for linear synchronous motor drive. IET Electric Power Applications, 2008,2(3),:201-214
37 Hunt KJ, Sbarbaro D. Neural networks for nonlinear internal model control. Proc. IEE. Pt,D,1991,138 :431-438
38宋崇智,吴玉国,王璐等.改进Elman网络在发动机齿轮箱故障诊断中的研究.仪器仪表学报. 2008, 29(7): 1413-1417
39 Cong S, Gao X P. Recurrent Neural Networks and Their Application in System Identification. Systems Engineering and Electronics. 2003,25(2):194-197
40 Cheng YuanChu, Qi WeiMin, Zhao Jie. A new Elman neural network and its dynamic properties. 2008 IEEE International Conference on Cybernetics and Intelligent Systems, CIS 2008,9:1110-1115
41曹小涛,李元春.受时变约束柔性臂鲁棒RBF神经网络力/位置控制.控制与决策. 2007, 22(9):977-988
42 D.A.Linkens, J.Nie Fuzzified RBF network based learning control:structure and selfconstruction. IEEE Int.Joint Conf. on Neural Networks,1993,1016-1021
43 D.A.Linkens, J.Nie. Fuzzified RBF network based learning control: structure and selfconstruction . IEEE Int.Joint Conf.on Neural Networks,1993,1016-1021
44陈勇强,刘开培.一种基于径向基函数动态阈值模型的机组状态监测方法.中国电机工程学报. 2007,27(26):96-102
45 Roy A, Govil S, Miranda R. A neural network learning theoryand polynomial time RBF algorithm. IEEE Trans. on Neural Network , 1997,8 (6):1301-1313
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