NDLI: Model reference neurocontrollers based on feedback linearization

Content Provider	IEEE Xplore Digital Library
Author	Hassibi, K.M. Loparo, K.A.
Copyright Year	1991
Description	Author affiliation: Case Western Reserve Univ., Cleveland, OH, USA (Hassibi, K.M.; Loparo, K.A.)
Abstract	The authors report experimental results on learning of the feedback linearizing control laws for an inverted pendulum system based on an unsupervised learning control scheme. Only the case where no state transformation is required for linearizing the nonlinear system is considered. A method inspired by a direct adaptive control scheme was used to learn the linearizing law using artificial neural networks. The main advantage of feedback linearizing control laws is that after linearization, which is not exact due to errors in computation and learning of the control law, the nonlinearities lie in the range-space of the input. This is important since various robust control techniques can be implemented as an outer loop such that the desired performance is guaranteed.
Starting Page	1813
Ending Page	1818
File Size	291808
Page Count	6
File Format	PDF
ISBN	0780302273
DOI	10.1109/IJCNN.1991.170688
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1991-11-18
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurocontrollers Neurofeedback Linear feedback control systems Control systems Control nonlinearities Unsupervised learning Nonlinear systems Adaptive control Artificial neural networks Error correction
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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