Adaptation and learning using multiple models, switching, and tuning
- 1 June 1995
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Control Systems
- Vol. 15 (3) , 37-51
- https://doi.org/10.1109/37.387616
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.< >Keywords
This publication has 16 references indexed in Scilit:
- Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networksPublished by Elsevier ,2003
- Intelligent control using fixed and adaptive modelsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Adaptive control of nonlinear multivariable systems using neural networksNeural Networks, 1994
- Improving transient response of adaptive control systems using multiple models and switchingIEEE Transactions on Automatic Control, 1994
- Universal approximation bounds for superpositions of a sigmoidal functionIEEE Transactions on Information Theory, 1993
- Control of nonlinear dynamical systems using neural networks: controllability and stabilizationIEEE Transactions on Neural Networks, 1993
- Disturbance rejection in nonlinear systems using neural networksIEEE Transactions on Neural Networks, 1993
- Applications of hysteresis switching in parameter adaptive controlIEEE Transactions on Automatic Control, 1992
- Identification and control of dynamical systems using neural networksIEEE Transactions on Neural Networks, 1990
- Design issues in adaptive controlIEEE Transactions on Automatic Control, 1988