Neural Network Control of Unknown Nonlinear Systems with Efficient Transient Performance

Authors:
Elias B. Kosmatopoulos;Diamantis Manolis;M. Papageorgiou
Affiliations:
Dynamic Systems and Simulation Laboratory Department of Production & Management Engineering, Technical University of Crete, Chania, Greece 73100;Dynamic Systems and Simulation Laboratory Department of Production & Management Engineering, Technical University of Crete, Chania, Greece 73100;Dynamic Systems and Simulation Laboratory Department of Production & Management Engineering, Technical University of Crete, Chania, Greece 73100
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Year:
2009

Citing 9
Cited 0

A universal construction of Artstein's theorem on nonlinear stabilization

Systems & Control Letters
Robust adaptive control

Robust adaptive control
Nonlinear system identification with recurrent neural networks and dead-zone Kalman filter algorithm

Neurocomputing
Brief paper: Image based visual servo control for a class of aerial robotic systems

Automatica (Journal of IFAC)
A Sum of Squares Approximation of Nonnegative Polynomials

SIAM Review
Large scale nonlinear control system fine-tuning through learning

IEEE Transactions on Neural Networks
Local compliance estimation via positive semidefinite constrained least squares

IEEE Transactions on Robotics
Approximation bounds for smooth functions in C(Rd) by neural and mixture networks

IEEE Transactions on Neural Networks
High-order neural network structures for identification of dynamical systems

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we provide a neural-based semi-global stabilization design for unknown nonlinear state-feedback stabilizable systems. The proposed design is shown to guarantee arbitrary good transient performance outside the regions where the system is uncontrollable. This is made possible through an appropriate combination of recent results developed by the author in the areas of adaptive control and adaptive optimization and a new result on the convex construction of Control Lyapunov Functions (CLF) for nonlinear systems.