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

doi:10.1007/s12206-022-0234-3

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	Adaptive local approximation neural network control based on extraordinariness particle swarm optimization for robotic manipulators
	H. Y. Sai; Z. B. Xu; C. Xu; X. M. Wang; K. Wang and L. Zhu
	2022
发表期刊	Journal of Mechanical Science and Technology
ISSN	1738-494X
卷号	36 期号:3 页码:1469-1483
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.
DOI	10.1007/s12206-022-0234-3
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收录类别	sci ; ei
语种	英语
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/66384
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	H. Y. Sai,Z. B. Xu,C. Xu,et al. Adaptive local approximation neural network control based on extraordinariness particle swarm optimization for robotic manipulators[J]. Journal of Mechanical Science and Technology,2022,36(3):1469-1483.
APA	H. Y. Sai,Z. B. Xu,C. Xu,X. M. Wang,&K. Wang and L. Zhu.(2022).Adaptive local approximation neural network control based on extraordinariness particle swarm optimization for robotic manipulators.Journal of Mechanical Science and Technology,36(3),1469-1483.
MLA	H. Y. Sai,et al."Adaptive local approximation neural network control based on extraordinariness particle swarm optimization for robotic manipulators".Journal of Mechanical Science and Technology 36.3(2022):1469-1483.

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