抛光力实时控制策略研究

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抛光力实时控制策略研究

张雷,周宛松,卢磊,樊成

吉林大学机械科学与工程学院

收稿日期:2013-12-04 修回日期:2014-02-09 发布日期:2014-09-12
通讯作者: 张雷

Research on real-time control strategies of polishing force

Received:2013-12-04 Revised:2014-02-09 Published:2014-09-12

摘要/Abstract

摘要： 针对传统抛光过程中的力位耦合问题，构建力位解耦的抛光力气动伺服控制系统。利用数据采集卡和Matlab/simulink的实时工具箱RTW，分别采用传统PID控制、前馈PID控制和单神经元PID控制三种控制策略，研究抛光力气动伺服系统实时控制效果。采用在线最小递归二乘法对前馈PID控制策略的参数实现精确辨识。采用有监督的Hebb学习规则调整单神经元PID加权系数。抛光力实时控制实验结果表明，前馈PID控制和单神经元PID控制与传统PID控制相比，能达到更好的控制效果。前馈PID控制达到稳定力输出所需时间短，单神经元PID控制达到力稳定的过程中振荡很小。

关键词: 抛光力控制, 力位解耦, 前馈PID, 单神经元PID, 在线辨识

Abstract: A force-position decoupling penumatic servo control system for polishing force is set up to solve the problem of force-position coupling in the traditional polishing process. The real-time control results of penumatic servo control system for polishing force are investigated by adopting traditional PID control, feedward PID control and single neuron PID control strategies with data acquisition card and RTW toolbox in Matlab/simulink. The parameters in feedward PID control can be identified by a recursive least squares algorithm. The weighting coefficients in single neuron PID control can be modified by the supervision Hebb learning regulations. The real-time control experimental results of polishing force indicate that the better control can be achieved by feedward PID control and single neuron PID control strategies than by traditional PID control. The duration for stablizing the polishing force is short by feedward PID control and the oscillation is little during the stablizing process of polishing force by single neuron PID control.

Key words: Polishing force control, Force-position decoupling, Feedward PID, Single neuron PID, On-line identification

张雷周宛松卢磊樊成. 抛光力实时控制策略研究[J]. .

参考文献

[1]Zhang L，Tam H Y，Yuan C M，et al.An investigation of material removal in polishing with fixed abrasives[J].Proceedings of the Institution of Mechanical Engineers，Part B：Journal of Engineering Manufacture，2002,216（1）：103-112.
[2]吴昌林，陈义. 铝轮曲面主被动柔顺控制抛光方法[J].中国机械工程，2009，20（23）:2821-2824.
(Wu Chang-lin，Chen Yi. Polishing method of aluminum alloy Wheel Surface with Active-passive compliant control[J]. China Mechanical Engineering，2009，20（23）: 2821-2824.)
[3]叶正茂，赵慧，张尚盈，等. 基于位置内环的柔顺力控制的研究[J]. 控制与决策，2006，21（6）：651-655.（Ye Zheng-mao，Zhao Hui，Zhang Shang-ying，et al. On compliant force control based on inner position loop[J]. Control and Decision，2006，21（6）：651-655.）
[4]殷跃红，尉忠信，朱剑英. 机器人柔顺控制研究[J]. 机器人，1998，20（3）:232-240.
(Yin Yue-hong，Wei Zhong-xin，Zhu Jian-ying. Compliance control of robot an overview [J]. Robot，1998，20（3）: 232-240.)
[5]Takosoglu J E，Dindorf R F, Laski P A.Rapid prototyping of fuzzy controller pneumatic servo system[J].The International Journal of Advanced ManufacturingTechnology，2009，40（3-4）：349-361.
[6]Ahn K K，Truong D Q.Online tuning fuzzy PID controller using robust Extended Kalman filter [J]. Journal of Process Control，2009，19（6）：1011-1023.
[7]Anh H P H.Online tuning gain scheduling MIMO neural PID control of the 2-axes pneumatic artifical muscle（PAM）robot arm [J]. Expert Systems with Applications，2010，37（9）：6547-6560.
[8]Thanh T U，Ahn K K.Nonlinear PID control to improve the control performance of 2 axes pneumatic artificial muscle manipulator using neural network [J]. Mechatronics，2006，16（9）：577-587.
[9]Wang X，Peng G.Modeling and control for pneumatic manipulator based on dynamic neural network[c]//Systems，Man and Cybernetics，2003.IEEE International Conference on.IEEE，2003,3:2231-2236.
[10]Kaitwanidvilai S，Parnichkun M.Force control in a pneumatic system using hybrid adaptive neuro-fuzzy model reference control[J]. Mechatronics，2005，15（1）：23-41.
[11]Richer E，Hurmuzlu Y.A high performance pneumatic force actuator system, Part 2：nonlinear Controller Design[J]. J. Dynamic Systems Measurement，and Control，2000，122(3)： 426-434.
[12]Saleem A，Abdrabbo S，Tutunji T.On-line identifcation and control of pneumatic servo drives via a mix-reality environment[J].The International Journal of Advanced Manufacturing Technology，2009,40（5-6）:518-530.