Abstract
In this paper, an adaptive controller is developed for a class of multi-input and multioutput nonlinear systems with neural networks (NNs) used as a modeling tool. It is shown that all the signals in the closed-loop system with the proposed adaptive neural controller are globally uniformly bounded for any external input in L0,∞. In our control design, the upper bound of the NN modeling error and the gains of external disturbance are characterized by unknown upper bounds, which is more rational to establish the stability in the adaptive NN control. Filter-based modification terms are used in the update laws of unknown parameters to improve the transient performance. Finally, fault-tolerant control is developed to accommodate actuator failure. An illustrative example applying the adaptive controller to control a rigid robot arm shows the validation of the proposed controller.
| Original language | English |
|---|---|
| Pages (from-to) | 2605-2613 |
| Number of pages | 9 |
| Journal | IEEE Transactions on Neural Networks and Learning Systems |
| Volume | 28 |
| Issue number | 11 |
| Early online date | 23 Aug 2016 |
| DOIs | |
| Publication status | Published - Nov 2017 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2016 IEEE.
Funding
This work was supported in part by the Major State Basic Research Development Program 973 under Grant 2012CB215202 and in part by the National Natural Science Foundation of China under Grant 61134001.
Keywords
- Fault-tolerant control
- Filter-based modification
- Globally uniformly bounded
- Neural networks (NNs)