Adaptive output feedback control methodology applicable to non-minimum phase nonlinear systems

Authors:
Naira Hovakimyan;Bong-Jun Yang;Anthony J. Calise
Affiliations:
Virginia Polytechnic Institute & State University, Blacksburg, VA 24061-0203, USA;Georgia Institute of Technology, Atlanta, GA 30332-0150, USA;Georgia Institute of Technology, Atlanta, GA 30332-0150, USA
Venue:
Automatica (Journal of IFAC)
Year:
2006

Citing 12
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Abstract

An adaptive output feedback control methodology is developed for a class of uncertain multi-input multi-output nonlinear systems using linearly parameterized neural networks. The methodology can be applied to non-minimum phase systems if the non-minimum phase zeros are modeled to a sufficient accuracy. The control architecture is comprised of a linear controller and a neural network. The neural network operates over a tapped delay line of memory units, comprised of the system's input/output signals. The adaptive laws for the neural-network weights employ a linear observer of the nominal system's error dynamics. Ultimate boundedness of the error signals is shown through Lyapunov's direct method. Simulations of an inverted pendulum on a cart illustrate the theoretical results.