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| Content Provider | Springer Nature Link |
|---|---|
| Author | Lee, Ching Hung Chang, Hua Hsiang |
| Copyright Year | 2013 |
| Abstract | In this paper, an adaptive backstepping control problem is proposed for a class of multiple-input-multiple-output nonlinear non-affine uncertain systems. An output recurrent wavelet neural network (ORWNN) is used to approximate the unknown nonlinear functions to develop the proposed adaptive backstepping controller. The proposed ORWNN combines the advantages of wavelet-based neural network, fuzzy neural network, and output feedback layer to achieve higher approximation accuracy and faster convergence. According to the estimation of ORWNN, the control scheme is designed by backstepping approach such that the system outputs follow the desired trajectories. Based on the Lyapunov approach, our approach guarantees that the system outputs converge to a small neighborhood of the references signals, that is, all signals of the closed-loop system are semi-globally uniformly ultimately bounded. Finally, simulation results including double pendulums system and two inverted pendulums on carts system are shown to demonstrate the performance and effectiveness of our approach. |
| Starting Page | 1035 |
| Ending Page | 1045 |
| Page Count | 11 |
| File Format | |
| ISSN | 09410643 |
| Journal | Neural Computing and Applications |
| Volume Number | 24 |
| Issue Number | 5 |
| e-ISSN | 14333058 |
| Language | English |
| Publisher | Springer London |
| Publisher Date | 2013-01-09 |
| Publisher Place | London |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Nonlinear non-affine Wavelet neural network Backstepping Adaptive control Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Software |
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