title: Robust output feedback control of nonlinear stochastic systems using neural networks.
abstract: 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.
authors.affiliation: Dipt. di Informatica e Sistemistica, La Sapienza Univ., Rome, Italy.
authors.forename: S
authors.initials: S
authors.lastname: Battilotti
authors.forename: A
authors.initials: A
authors.lastname: De Santis