李芳, 陈奇, 刘凯, 吴阳, 陈伊宁, 王明昕, 姚佳烽. 气动人工肌肉驱动的并联平台模糊PID控制[J]. 机器人, 2021, 43(2): 140-147. DOI: 10.13973/j.cnki.robot.200175
引用本文: 李芳, 陈奇, 刘凯, 吴阳, 陈伊宁, 王明昕, 姚佳烽. 气动人工肌肉驱动的并联平台模糊PID控制[J]. 机器人, 2021, 43(2): 140-147. DOI: 10.13973/j.cnki.robot.200175
LI Fang, CHEN Qi, LIU Kai, WU Yang, CHEN Yining, WANG Mingxin, YAO Jiafeng. Fuzzy PID Control of Parallel Platform Actuated by Pneumatic Artificial Muscle[J]. ROBOT, 2021, 43(2): 140-147. DOI: 10.13973/j.cnki.robot.200175
Citation: LI Fang, CHEN Qi, LIU Kai, WU Yang, CHEN Yining, WANG Mingxin, YAO Jiafeng. Fuzzy PID Control of Parallel Platform Actuated by Pneumatic Artificial Muscle[J]. ROBOT, 2021, 43(2): 140-147. DOI: 10.13973/j.cnki.robot.200175

气动人工肌肉驱动的并联平台模糊PID控制

Fuzzy PID Control of Parallel Platform Actuated by Pneumatic Artificial Muscle

  • 摘要: 为了提高气动人工肌肉(PAM)驱动的3自由度并联平台的位置跟踪控制精度,提出一种基于Prandtl-Ishlinskii(P-I)逆模型的模糊PID(比例-积分-微分)控制算法.首先通过动态试验分析单根PAM的迟滞特性,建立P-I逆模型.接着搭建PAM驱动的3自由度并联试验平台,设计模糊PID控制器.试验结果显示模型最大误差为0.3904 mm,平均误差为0.0793 mm,验证了所提控制算法能够很大程度地减小PAM迟滞特性对动态控制精度的影响.3自由度平台的γ角误差能保持在0.13°以内,基于P-I逆模型的模糊PID控制器整体误差波动小,表明动态轨迹跟踪效果较好.

     

    Abstract: A fuzzy PID (proportional-integral-differential) control algorithm based on Prandtl-Ishlinskii (P-I) inverse model is proposed to improve the control accuracy of position tracking for a 3-DOF (degree of freedom) parallel platform actuated by PAM (pneumatic artificial muscle). Firstly, the hysteresis characteristics of a single pneumatic artificial muscle are analyzed through dynamic tests to establish P-I inverse model. Then, the PAM actuated 3-DOF parallel test platform is built, and the fuzzy PID controller is designed. Tests result show that the maximum error of the model is 0.3904 mm, and the average error is 0.0793 mm, which verifies that the proposed control algorithm can greatly reduce the influence of PAM hysteresis characteristics on the dynamic control accuracy. And the error of angle γ of the 3-DOF platform can be kept within 0.13°, and the overall error fluctuation of the fuzzy PID controller based on P-I inverse model is small, which shows that the effect of dynamic trajectory tracking is good.

     

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