串联机器人轨迹跟踪控制模糊自适应PID算法的误差修正

提出了一种基于改进PID控制算法的串联机器人轨迹跟踪控制策略，首先采用减聚类的方法和改进的Logistic映射对RBF神经网络进行聚类中心的优化，然后将改进RBF神经网络中的自适应学习机制和自调整能力应用于传统PID控制算法中，对PID控制算法进行最优PID控制参数的选取。仿真实验表明，提出的串联机器人轨迹跟踪控制策略相比较传统PID控制算法，其误差更小，精度更高。...

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Bibliographic Details
Published in电子技术应用 Vol. 41; no. 1; pp. 60 - 63
Main Author 赫建立 朱龙英 成磊 郑帅 陆宝发
Format Journal Article
LanguageChinese
Published 常州大学机械工程学院,江苏常州,213164%盐城工学院汽车工程学院,江苏盐城,224001%安徽理工大学机械工程学院,安徽淮南,232001%江苏大学机械工程学院,江苏镇江,212013 2015
Subjects
PID控制算法
RBF神经网络
串联机器人
误差修正
轨迹跟踪控制
trajectory tracking control
RBF神经网络
误差修正
RBF neural network
轨迹跟踪控制
PID control algorithm
串联机器人
series robot
PID控制算法
error correction
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ISSN0258-7998
DOI10.16157/j.cnki.0258-7998.2014072302730

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Summary:提出了一种基于改进PID控制算法的串联机器人轨迹跟踪控制策略，首先采用减聚类的方法和改进的Logistic映射对RBF神经网络进行聚类中心的优化，然后将改进RBF神经网络中的自适应学习机制和自调整能力应用于传统PID控制算法中，对PID控制算法进行最优PID控制参数的选取。仿真实验表明，提出的串联机器人轨迹跟踪控制策略相比较传统PID控制算法，其误差更小，精度更高。
Bibliography:PID control algorithm ; RBF neural network ; error correction ; series robot ; trajecto13~ tracking control
This paper proposed a trajectory tracking control strategy of serial robot based on RBF neural network optimized PID control algorithm. The adaptive learning mechanism neural network and self adjusting ability in the RBF were applied to traditional PID control algorithm. The optimal parameters of the PID control algorithm was selected. Compared with traditional PID control algo- rithm, the simulation experiments showed that the proposed optimized PID control algorithm based on RBF neural network in series robot trajectory tracking control strategy had smaller error and higher accuracy.
He Jianli,Zhu Longying, Cheng Lei,Zheng Shuai, Lu Baofa （1.School of Mechanical Engineering, Changzhou University, Changzhou 213164, China; 2.School of Automotive Engineering, Yancheng Institute of Technology, Yancheng 224001 , China ; School of Mechanical Engineering,Anhui University of Science And Technology, Huainan 2320
ISSN:0258-7998
DOI:10.16157/j.cnki.0258-7998.2014072302730