Adaptive local approximation neural network control based on extraordinariness particle swarm optimization for robotic manipulators

被引：0
作者：
Huayang Sai
Zhenbang Xu
Ce Xu
Xiaoming Wang
Kai Wang
Lin Zhu
机构：
[1] Chinese Academy of Science,Changchun Institute of Optics, Fine Mechanics and Physics
[2] University of Chinese Academy of Sciences,The Center of Materials Science and Optoelectronics Engineering
[3] University of Chinese Academy of Sciences,undefined
来源：
Journal of Mechanical Science and Technology | 2022年 / 36卷
关键词：
Adaptive neural network; Robotic manipulator; EPSO; Position and velocity tracking; Task space;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, an adaptive radial basis function neural network (RBFNN) controller based on extraordinariness particle swarm optimization (EPSO) is proposed. To improve the trajectory tracking performance of robotic manipulators, the uncertainties of the manipulator dynamic equation are locally approximated using three RBFNNs with optimized hyperparameters. Besides, a robust control item is also considered in the controller to resist external disturbances. During hyperparameters optimization, the EPSO optimizer iteratively optimizes the hyperparameters of the RBFNN controller using the composite error of the system output. The stability of the control scheme is analyzed with the Lyapunov stability. Simulation results as well as the experimental verification prove the efficiency and applicability of the control scheme.
引用
收藏
页码：1469 / 1483
页数：14
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