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![]() |
刊名 | International Journal of Robotics and Automation
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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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