Robust neural adaptive stabilization of unknown systems withmeasurement noise

Authors:
G. A. Rovithakis
Affiliations:
Dept. of Electron. & Comput. Eng., Tech. Univ. of Crete, Chania
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Year:
1999

Citing 0
Cited 8

H∞ Control Design Using Dynamic Neural Networks

Neural Processing Letters
Approximate adaptive output feedback stabilization via passivation of MIMO uncertain systems using neural networks

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Constrained PI tracking control for output probability distributions based on two-step neural networks

IEEE Transactions on Circuits and Systems Part I: Regular Papers
Adaptive neural network tracking control with disturbance attenuation for multiple-input nonlinear systems

IEEE Transactions on Neural Networks
Adaptive statistic tracking control based on two-step neural networks with time delays

IEEE Transactions on Neural Networks
Recurrent neural networks for nonlinear output regulation

Automatica (Journal of IFAC)
Stable adaptive neuro-control design via Lyapunov function derivative estimation

Automatica (Journal of IFAC)
Adaptive control design using stability analysis and tracking errors dynamics for nonlinear square MIMO systems

Engineering Applications of Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we consider the problem of adaptive stabilizing unknown nonlinear systems whose state is contaminated with external disturbances that act additively. A uniform ultimate boundedness property for the actual system state is guaranteed, as well as boundedness of all other signals in the closed loop. It is worth mentioning that the above properties are satisfied without the need of knowing a bound on the “optimal” weights, providing in this way higher degrees of autonomy to the control system. Thus, the present work can be seen as a first approach toward the development of practically autonomous systems