Adaptive neural network tracking control with disturbance attenuation for multiple-input nonlinear systems

Authors:
Artemis K. Kostarigka;George A. Rovithakis
Affiliations:
Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece;Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
Venue:
IEEE Transactions on Neural Networks
Year:
2009

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Abstract

A switching adaptive neural network controller for multiple-input nonlinear, affine in the control dynamical systems with unknown nonlinearities is designed, capable of arbitrarily attenuating L2 or L∞ external disturbances. In the absence of disturbances, a uniform ultimate boundedness property of the tracking error with respect to an arbitrarily small set around the origin is guaranteed, as well as the uniform boundedness of all the signals in the closed loop. The proposed switching adaptive controller effectively avoids possible division by zero, while guaranteeing the continuity of switching. In this way, problems connected to existence of solutions and chattering phenomena are alleviated. Simulations illustrate the approach.