中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Minimum parameter learning method for an N-link manipulator with nonlinear disturbance observer

文献类型:期刊论文

作者Hongjun Yang; Jinkun Liu
刊名International Journal of Robotics and Automation
出版日期2016
卷号31期号:3页码:206-212
关键词Minimum Parameter Learning Adaptive Control Disturbance Observer Rbf Neural Networks N-link Manipulator
英文摘要

This paper focuses on designing an adaptive Radial basis function neural network (RBF NN) control method for an n-link robot manipulator in the presence of unknown parameters and disturbances. A minimum parameter learning method that observably reduces the online computational burden is used to estimate the maximum norm of ideal RBF NN weight vectors. The unknown disturbances are compensated by an exponential disturbance observer (asymptotic nonlinear disturbance estimator with exponential decaying error), which does not require the knowledge of the bound of disturbances and the measurement of acceleration signals. The closed-loop system is proved uniformly ultimately bounded with the developed adaptive RBF NN controller and disturbance observer. A two-link robot manipulator is taken for simulation. Both the theoretical analysis and simulations validate the effectiveness of the developed scheme.

源URL[http://ir.ia.ac.cn/handle/173211/25789]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
通讯作者Jinkun Liu
作者单位Beihang University
推荐引用方式
GB/T 7714
Hongjun Yang,Jinkun Liu. Minimum parameter learning method for an N-link manipulator with nonlinear disturbance observer[J]. International Journal of Robotics and Automation,2016,31(3):206-212.
APA Hongjun Yang,&Jinkun Liu.(2016).Minimum parameter learning method for an N-link manipulator with nonlinear disturbance observer.International Journal of Robotics and Automation,31(3),206-212.
MLA Hongjun Yang,et al."Minimum parameter learning method for an N-link manipulator with nonlinear disturbance observer".International Journal of Robotics and Automation 31.3(2016):206-212.

入库方式: OAI收割

来源:自动化研究所

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