Adaptive control of a class of nonaffine systems using neural networks.
A neural control synthesis method is considered for a class of nonaffine uncertain single-input-single-output (SISO) systems. The method eliminates a fixed-point assumption and does not assume boundedness on the time derivative of a control effectiveness term. One or the other of these assumptions exist in earlier papers on this subject. Using Lyapunov's direct method, it is shown that all the signals of the closed-loop system are uniformly ultimately bounded, and that the tracking error converges to an adjustable neighborhood of the origin. Simulation with a Van Der Pol equation with nonaffine control terms illustrates the approach.
School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA. jun.yang@ae.gatech.edu
Bong-Jun
BJ
Yang
Anthony J
AJ
Calise
D000465:Algorithms; D003198:Computer Simulation; D003661:Decision Support Techniques; D005246:Feedback; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics