Robust Adaptive Control for Robot Manipulator via Fuzzy Neural Network
In this paper, a robust adaptive fuzzy neural control approach is developed for the trajectory tracking problems of the robot manipulator with external disturbances and parametric uncertainties. Considering the existence of nominal model, an adaptive fuzzy neural network (FNN) is used only to approximate the model uncertainties. A robust control component is designed to compensate the approximate error. Different from the previous work, the proposed method need no a prior information of the approximate error bound. Both the error bound and the FNN parameters are tuned online in the sense of the Lyapunov synthetic method, which ensures all signals in the closed loop are uniformly ultimately bounded. Computer simulation results of a two-link robot manipulator demonstrate the effectiveness of the proposed approach.
Adaptive control robust control fuzzy neural network robot manipulator
Xiaoyu Liu Kangling Fang Xinhai Liu
Wuhan University of Science & Technology
国际会议
2006现代科技国际研讨会(The International Workshop on Modern Science and Technology in 2006)
北京
英文
425-430
2006-04-01（万方平台首次上网日期，不代表论文的发表时间）