Title :
A robust neural adaptive control scheme
Author :
Rovithakis, George A. ; Christodoulou, Manolis A.
Author_Institution :
Dept. of Electron. & Comput. Eng., Tech. Univ. of Crete, Chania, Greece
Abstract :
A direct nonlinear adaptive controller, to solve the regulation problem for unknown dynamical systems that are modeled by recurrent neural networks is discussed. The behaviour of the closed loop system is analyzed for the case in which the true system differs from the recurrent neural network due to the presence of a modeling error term. Convergence of the state to zero plus boundedness of all signals in the closed loop is guaranteed provided that a complete matching at zero property is satisfied. However, if the above assumption is no longer valid, the authors´ adaptive regulator can still guarantee uniform boundedness with the addition of appropriately modified update laws. Furthermore, the magnitude of the growth of the modeling error is considered unknown
Keywords :
adaptive control; closed loop systems; convergence; neurocontrollers; nonlinear control systems; nonlinear dynamical systems; recurrent neural nets; robust control; uncertain systems; closed loop system; complete matching; direct nonlinear adaptive controller; modeling error; recurrent neural networks; robust neural adaptive control scheme; uniform boundedness; unknown dynamical systems; Adaptive control; Closed loop systems; Computer networks; Neural networks; Neurons; Nonlinear control systems; Programmable control; Recurrent neural networks; Robust control; Robust stability;
Conference_Titel :
Decision and Control, 1995., Proceedings of the 34th IEEE Conference on
Conference_Location :
New Orleans, LA
Print_ISBN :
0-7803-2685-7
DOI :
10.1109/CDC.1995.480607
