Accessibility navigation


A hyperstable neural network for the modelling and control of nonlinear systems

Warwick, K., Zhu, Q. M. and Ma, Z. (2000) A hyperstable neural network for the modelling and control of nonlinear systems. Sadhana: Academy Proceedings in Engineering Sciences, 25 (2). pp. 169-180. ISSN 0256-2499

Full text not archived in this repository.

To link to this article DOI: 10.1007/BF02703757

Abstract/Summary

A multivariable hyperstable robust adaptive decoupling control algorithm based on a neural network is presented for the control of nonlinear multivariable coupled systems with unknown parameters and structure. The Popov theorem is used in the design of the controller. The modelling errors, coupling action and other uncertainties of the system are identified on-line by a neural network. The identified results are taken as compensation signals such that the robust adaptive control of nonlinear systems is realised. Simulation results are given.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:17797
Uncontrolled Keywords:Computer control, neural networks, nonlinear systems, adaptive, control
Publisher:Springer

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation