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Adaptation and learning using multiple models, switching, and tuning
Abstract
This article presents a general methodology for the design of adaptive control systems which can learn to operate efficiently in dynamical environments possessing a high degree of uncertainty. Multiple models are used to describe the different environments and the control is effected by switching to an appropriate controller followed by tuning or adaptation. The study of linear systems provides the theoretical foundation for the approach and is described first. The manner in which such concepts can be extended to the control of nonlinear systems using neural networks is considered next. Towards the end of the article, the applications of the above methodology to practical robotic manipulator control is described.>
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Cited by
A unifying computational framework for motor control and social interaction
Wolpert, D. M., Doya, K., & Kawato, M. (2003) · Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rstb.2002.1238 · Cited by 1,228
Model learning for robot control: A survey
Nguyen-Tuong, D., & Peters, J. (2011) · Cognitive Processing · doi:10.1007/s10339-011-0404-1 · Cited by 503
Multiple model-based reinforcement learning
Doya, K., Samejima, K., Katagiri, K.-I., & Kawato, M. (2002) · Neural Computation · doi:10.1162/089976602753712972 · Cited by 476
Metadata
- Venue
- IEEE Control Systems
- Published
- 1995-06
- Volume
- 15
- Issue
- 3
- Pages
- 37-51
- ISSN
- 1066-033X (ISSN-L)
- 1941-000X
- OpenAlex
- W2069913713
- Cited by
- 442
- References
- 0
- FWCI
- 23.77
- Type
- journal article
- Language
- en
- Open access
- closed
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- Adaptation and learning using multiple models, switching, and tuning. (1995). IEEE Control Systems. https://doi.org/10.1109/37.387616
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
