Output tracking with constrained inputs via inverse optimal adaptive recurrent neural control

Authors:
Luis J. Ricalde;Edgar N. Sanchez
Affiliations:
Facultad de Ingenieria de la Universidad Autonoma de Yucatan, Av. Industrias no Contaminantes por Periferico Norte, Apdo. Postal 150 Cordemex, Merida, Yucatan, Mexico;CINVESTAV, Unidad Guadalajara, Apartado Postal 31-438, Plaza La Luna, Guadalajara, Jalisco, C.P. 45091, Mexico
Venue:
Engineering Applications of Artificial Intelligence
Year:
2008

Citing 6
Cited 1

Stable adaptive systems

Stable adaptive systems
A universal formula for stabilization with bounded controls

Systems & Control Letters
Dynamical neural networks that ensure exponential identification error convergence

Neural Networks
Stabilization of Nonlinear Uncertain Systems

Stabilization of Nonlinear Uncertain Systems
Chaos control and synchronization, with input saturation, via recurrent neural networks

Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Identification and control of dynamical systems using neural networks

IEEE Transactions on Neural Networks

Discrete-time inverse optimal neural control for synchronous generators

Engineering Applications of Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper extends previous results to the output tracking problem of nonlinear systems with unmodelled dynamics and constrained inputs. A recurrent high order neural network is used to identify the unknown system dynamics and a learning law is obtained using the Lyapunov methodology. A stabilizing control law for the output tracking error dynamics is developed using the Lyapunov methodology and the Sontag control law for nonlinear systems with constrained inputs.