In this paper, we present two on-line adaptive control algorithms for non-linear plants using neural networks. The architecture used is based on the concept of specialized learning, which was first proposed by Psaltis et al. and suffers from two main problems, namely lack of knowledge of the plant J
Control of chaotic systems using an on-line trained linear neural controller
β Scribed by Keiji Konishi; Hideki Kokame
- Publisher
- Elsevier Science
- Year
- 1997
- Tongue
- English
- Weight
- 767 KB
- Volume
- 100
- Category
- Article
- ISSN
- 0167-2789
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β¦ Synopsis
We propose a control system including an on-line trained linear neural controller to control chaotic systems. The control system stabilizes a chaotic orbit onto an unstable fixed point without using the knowledge of the location of the point and the local linearized dynamics at the point. Furthermore, the control system can track the stabilized orbit to the unstable fixed point whose location and local dynamics vary slowly with a variation of the system parameter. This paper extends a previous paper (Konishi and Kokame, 1995) tbr more general situations and improves the neural controller proposed in the previous paper both to simplify the training algorithm and to guarantee the convergence of the neural controller. The stability analysis of the control system reveals that some unstable fixed points cannot be stabilized in the control system. Numerical experiments show that the control system works well for controlling high-dimensional chaotic systems.
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