Adaptive output feedback control of nonlinear systems using neural networks

Authors:
Anthony J. Calise;Naira Hovakimyan;Moshe Idan
Affiliations:
School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0150, USA;School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0150, USA;School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0150, USA
Venue:
Automatica (Journal of IFAC)
Year:
2001

Citing 9
Cited 17

Multilayer feedforward networks are universal approximators

Neural Networks
On the approximate realization of continuous mappings by neural networks

Neural Networks
Universal approximation using radial-basis-function networks

Neural Computation
Stabilization by output feedback for systems with ISS inverse dynamics

Systems & Control Letters
Robust adaptive control

Robust adaptive control
Brief A combined backstepping and small-gain approach to adaptive output feedback control

Automatica (Journal of IFAC)
Output feedback control of nonlinear systems using RBF neural networks

IEEE Transactions on Neural Networks
Adaptive output feedback control of uncertain nonlinear systems using single-hidden-layer neural networks

IEEE Transactions on Neural Networks
Gaussian networks for direct adaptive control

IEEE Transactions on Neural Networks

Adaptive fuzzy controller for non-affine systems with zero dynamics

International Journal of Systems Science
Letters: NN-based output feedback adaptive variable structure control for a class of non-affine nonlinear systems: A nonseparation principle design

Neurocomputing
A composite parallel intelligent controller for multimodal and uncertain systems: design and development

CA '07 Proceedings of the Ninth IASTED International Conference on Control and Applications
A composite parallel intelligent controller for multimodal and uncertain systems: design and development

MNT '07 The IASTED International Conference on Modern Nonlinear Theory: Bifurcation and Chaos
Direct adaptive self-structuring fuzzy controller for nonaffine nonlinear system

Fuzzy Sets and Systems
A new neuroadaptive control architecture for nonlinear uncertain dynamical systems: beyond σ- and e-modifications

IEEE Transactions on Neural Networks
Decentralized adaptive control for a class of nonlinear continuous time interconnected systems using neural networks

Control and Intelligent Systems
Adaptive neural flight control system for helicopter

CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
Applying RBF neural network to missile control system parameter optimization

CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 2
Brief paper: Robust adaptive control of nonlinear non-minimum phase systems with uncertainties

Automatica (Journal of IFAC)
Robust decentralized direct adaptive output feedback fuzzy control for a class of large-sale nonaffine nonlinear systems

Information Sciences: an International Journal
An on-line learning neural controller for helicopters performing highly nonlinear maneuvers

Applied Soft Computing
Sequential support vector machine control of nonlinear systems by state feedback

ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Adaptive fuzzy output-feedback controller for SISO affine nonlinear systems without state observer

KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Non-linear adaptive controllers with linear dynamic compensator and neural network

ACMOS'09 Proceedings of the 11th WSEAS international conference on Automatic control, modelling and simulation
Adaptive output feedback control methodology applicable to non-minimum phase nonlinear systems

Automatica (Journal of IFAC)
Robust adaptive control of flexible link manipulators using multilayer perceptron

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

Quantified Score

Hi-index	22.15

Visualization

Abstract

A direct adaptive output feedback control design procedure is developed for highly uncertain nonlinear systems, that does not rely on state estimation. The approach is also applicable to systems of unknown, but bounded dimension. In particular, we consider single-input/single-output nonlinear systems, whose output has known, but otherwise arbitrary relative degree. This includes systems with both parameter uncertainty and unmodeled dynamics. The result is achieved by extending the universal function approximation property of linearly parameterized neural networks to model unknown system dynamics from input/output data. The network weight adaptation rule is derived from Lyapunov stability analysis, and guarantees that the adapted weight errors and the tracking error are bounded. Numerical simulations of an output feedback controlled van der Pol oscillator, coupled with a linear oscillator, is used to illustrate the practical potential of the theoretical results.