Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5004222 | ISA Transactions | 2016 | 11 Pages |
Abstract
This paper studies learning from adaptive neural network (NN) output feedback control of nonholonomic unicycle-type mobile robots. The major difficulties are caused by the unknown robot system dynamics and the unmeasurable states. To overcome these difficulties, a new adaptive control scheme is proposed including designing a new adaptive NN output feedback controller and two high-gain observers. It is shown that the stability of the closed-loop robot system and the convergence of tracking errors are guaranteed. The unknown robot system dynamics can be approximated by radial basis function NNs. When repeating same or similar control tasks, the learned knowledge can be recalled and reused to achieve guaranteed stability and better control performance, thereby avoiding the tremendous repeated training process of NNs.
Related Topics
Physical Sciences and Engineering
Engineering
Control and Systems Engineering
Authors
Wei Zeng, Qinghui Wang, Fenglin Liu, Ying Wang,