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A Q -Modification Neuroadaptive Control Architecture for Discrete-Time Systems

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2 Author(s)
Konstantin Y. Volyanskyy ; School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA ; Wassim M. Haddad

This brief extends the new neuroadaptive control framework for continuous-time nonlinear uncertain dynamical systems based on a Q -modification architecture to discrete-time systems. As in the continuous-time case, the discrete-time update laws involve auxiliary terms, or Q-modification terms, predicated on an estimate of the unknown neural network weights which in turn involve a set of auxiliary equations characterizing a set of affine hyperplanes. In addition, we show that the Q -modification terms in the discrete-time update law are designed to minimize an error criterion involving a sum of squares of the distances between the update weights and the family of affine hyperplanes.

Published in:

IEEE Transactions on Neural Networks  (Volume:21 ,  Issue: 9 )