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

文献类型：期刊论文

作者H. Y. Sai; Z. B. Xu; C. Xu; X. M. Wang; K. Wang and L. Zhu
刊名Journal of Mechanical Science and Technology
出版日期2022
卷号36期号:3页码:1469-1483
ISSN号1738-494X
DOI10.1007/s12206-022-0234-3
英文摘要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.
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语种英语
源URL[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.

入库方式： OAI收割

来源：长春光学精密机械与物理研究所

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