A Q-modification neuroadaptive control architecture for discrete-time systems.
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.
Volyanskyy
Konstantin Y
KY
Georgia Institute of Technology, Atlanta, 30332-0150, USA. gtg891s@mail.gatech.edu
Haddad
Wassim M
WM
D000222:Adaptation, Physiological / Q000502:physiology; D000465:Algorithms; D001185:Artificial Intelligence; D001331:Automation / Q000379:methods*; D003198:Computer Simulation / Q000592:standards*; D016571:Neural Networks, Computer; D013997:Time Factors