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Input/output linearization using dynamic recurrent neural networks

✍ Scribed by A. Delgado; C. Kambhampati; K. Warwick

Publisher
Elsevier Science
Year
1996
Tongue
English
Weight
408 KB
Volume
41
Category
Article
ISSN
0378-4754

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✦ Synopsis

This paper brings together two areas of research that have received considerable attention during the last years, namely feedback linearization and neural networks.

A proposition that guarantees the Input/Output (I/O) linearization of nonlinear control affine systems with Dynamic Recurrent Neural Networks (DRNNs) is formulated and proved. The proposition and the linearization procedure are illustrated with the simulation of a single link manipulator.

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✍ A. Yeşildirek; F.L. Lewis πŸ“‚ Article πŸ“… 1995 πŸ› Elsevier Science 🌐 English βš– 598 KB

For a class of single-input single-output continuous-time nonlinear systems, a multilayer neural network-based controller that feedback-linearizes the system is presented. Control action is used to achieve tracking performance for a state-feedback linearizable but unknown nonlinear system. The contr