We present an adaptive output feedback controller for a class of uncertain stochastic nonlinear systems. The plant dynamics is represented as a nominal linear system plus nonlinearities. In turn, these nonlinearities are decomposed into a part, obtained as the best approximation given by neural networks, plus a remaining part which is treated as uncertainties, modeling approximation errors, and neglected dynamics. The weights of the neural network are tuned adaptively by a Lyapunov design. The proposed controller is obtained through robust optimal design and combines together parameter projection, control saturation, and high-gain observers. High performances are obtained in terms of large errors tolerance as shown through simulations.
|Titolo:||Robust output feedback control of nonlinear systems using neural networks|
BATTILOTTI, Stefano (Corresponding)
|Data di pubblicazione:||2003|
|Citazione:||Robust output feedback control of nonlinear systems using neural networks / Battilotti, Stefano; DE SANTIS, Alberto. - In: IEEE TRANSACTIONS ON NEURAL NETWORKS. - ISSN 1045-9227. - STAMPA. - 14:1(2003), pp. 103-116.|
|Appare nella tipologia:||01a Articolo in rivista|