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Dynamic neural networks for output feedback control
Author(s) -
Hovakimyan Naira,
Rysdyk Rolf,
Calise Anthony J.
Publication year - 2001
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/1099-1239(200101)11:1<23::aid-rnc545>3.0.co;2-n
Subject(s) - control theory (sociology) , artificial neural network , nonlinear system , computer science , van der pol oscillator , inversion (geology) , bounded function , observer (physics) , controller (irrigation) , state observer , control engineering , control (management) , mathematics , engineering , artificial intelligence , paleontology , mathematical analysis , physics , quantum mechanics , structural basin , agronomy , biology
A dynamic neural network is designed to estimate velocities from displacement measurements for a nonlinear system. A linear‐in‐parameters NN is used for state reconstruction. Conditions are provided under which the estimation error is guaranteed to be ultimately bounded. Subsequently, this observer is integrated into an adaptive controller architecture. The controller is based on model inversion and is augmented with a second learning‐while‐controlling neural network. Conditions are derived which guarantee ultimate boundedness of all the errors in the combined observer–controller feedback system. Open‐ and closed‐loop simulations for a Van Der Pol oscillator are used to illustrate the results. Copyright © 2001 John Wiley & Sons, Ltd.