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
Neural adaptive control of non-linear plants via a multiple inverse model approach
✍ Scribed by Pedro J. Zufiria; Jesús Fraile-Ardanuy; Ricardo Riaza; Juan I. Alonso
- Publisher
- John Wiley and Sons
- Year
- 1999
- Tongue
- English
- Weight
- 284 KB
- Volume
- 13
- Category
- Article
- ISSN
- 0890-6327
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✦ Synopsis
In this paper, neural architectures for controlling non-linear plants with parameter variation are proposed. In the "rst part of the document, the concept of specialized learning over an operation region is considered in order to identify the inverse dynamics of a given plant. Some aspects concerning discretization and invertibility of continuous-time plants are also addressed. In the second part of this work, a control architecture which combines the former approach of inverse identi"cation through specialised learning with a multiple model scheme is presented. Finally, simulation results are discussed, evaluating the performance of the proposed schemes; speci"cally, the presented controllers are applied to the simulation of the control of a robot arm.
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