Adaptive feedback linearization control of chaotic systems via recurrent high-order neural networks

Authors:
Zhao Lu;Leang-San Shieh;Guanrong Chen;Norman P. Coleman
Affiliations:
Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77204-4005, USA;Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77204-4005, USA;Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong, PR China;US Army Armament Center, Dover, NJ 07801, USA
Venue:
Information Sciences: an International Journal
Year:
2006

Citing 6
Cited 12

Recursive neural networks for associative memory

Recursive neural networks for associative memory
Nonlinear regulation of a Lorenz system by feedback linearization techniques

Dynamics and Control
A detailed study of adaptive control of chaotic systems with unknown parameters

Dynamics and Control
Nonlinear Control Systems

Nonlinear Control Systems
Neural Networks for Identification, Prediction, and Control

Neural Networks for Identification, Prediction, and Control
High-order neural network structures for identification of dynamical systems

IEEE Transactions on Neural Networks

Feedback stabilization of dissipative impulsive dynamical systems

Information Sciences: an International Journal
Software architecture graphs as complex networks: A novel partitioning scheme to measure stability and evolution

Information Sciences: an International Journal
Application of adaptive control to the fluctuation of engine speed at idle

Information Sciences: an International Journal
Locally recurrent neural networks for wind speed prediction using spatial correlation

Information Sciences: an International Journal
Stability analysis of high-order Hopfield type neural networks with uncertainty

Neurocomputing
pth moment stability analysis of stochastic recurrent neural networks with time-varying delays

Information Sciences: an International Journal
Self-tuning fuzzy sliding-mode control for time-delay chaotic systems

ACACOS'08 Proceedings of the 7th WSEAS International Conference on Applied Computer and Applied Computational Science
EP-based kinematic control and adaptive fuzzy sliding-mode dynamic control for wheeled mobile robots

Information Sciences: an International Journal
Global stability analysis of a general class of discontinuous neural networks with linear growth activation functions

Information Sciences: an International Journal
Non-affine nonlinear adaptive control of decentralized large-scale systems using neural networks

Information Sciences: an International Journal
Feedback linearizing indirect adaptive fuzzy control with foraging based on-line plant model estimation

Applied Soft Computing
Synchronization control of a class of memristor-based recurrent neural networks

Information Sciences: an International Journal

Quantified Score

Hi-index	0.07

Visualization

Abstract

In the realm of nonlinear control, feedback linearization via differential geometric techniques has been a concept of paramount importance. However, the applicability of this approach is quite limited, in the sense that a detailed knowledge of the system nonlinearities is required. In practice, most physical chaotic systems have inherent unknown nonlinearities, making real-time control of such chaotic systems still a very challenging area of research. In this paper, we propose using the recurrent high-order neural network for both identifying and controlling unknown chaotic systems, in which the feedback linearization technique is used in an adaptive manner. The global uniform boundedness of parameter estimation errors and the asymptotic stability of tracking errors are proved by the Lyapunov stability theory and the LaSalle-Yoshizawa theorem. In a systematic way, this method enables stabilization of chaotic motion to either a steady state or a desired trajectory. The effectiveness of the proposed adaptive control method is illustrated with computer simulations of a complex chaotic system.