Stable Adaptive Control of A Class of Nonlinear Dynamic Systems Using RBF Networks
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 15-26 |
| Journal / Publication | Intelligent Automation and Soft Computing |
| Volume | 2 |
| Issue number | 1 |
| Publication status | Published - 1996 |
| Externally published | Yes |
Link(s)
Abstract
Based on Radial Basis Function neural networks, a stable adaptive control scheme is proposed for a class of unknown nonlinear dynamic systems. It is shown that the scheme is stable and convergent in the sense that all the signal in the loop remain bounded, and in the ideal case, output tracking error will converge to zero and in the non-ideal case, the error will converge to a residue which is proportional to the modelling error bound. Simulation results are also provided to demonstrate performance of the scheme.
Research Area(s)
- Adaptive control, Neural networks, Nonlinear systems, Radial basis functions, Stability
Citation Format(s)
Stable Adaptive Control of A Class of Nonlinear Dynamic Systems Using RBF Networks. / Feng, G.; Zhang, N.; Chak, C. K. et al.
In: Intelligent Automation and Soft Computing, Vol. 2, No. 1, 1996, p. 15-26.
In: Intelligent Automation and Soft Computing, Vol. 2, No. 1, 1996, p. 15-26.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
