Neural network-based adaptive sliding mode control for uncertain non-linear MIMO systems
by Noureddine Goléa; Ghania Debbache; Amar Goléa
International Journal of Modelling, Identification and Control (IJMIC), Vol. 16, No. 4, 2012

Abstract: The purpose of this paper is the design of neural network-based adaptive sliding mode controller (NASMC) for uncertain unknown MIMO non-linear systems. A special architecture adaptive neural network, with hyperbolic tangent activation functions, is used to emulate the equivalent and switching control terms of the classic sliding mode controller (SMC). The bounded motion of the system around the sliding surface and the stability of the global system, in the sense that all signals remain bounded, are guaranteed. Unlike other works, this is not a combination of neural networks and SMC approaches, but a new implementation of adaptive SMC using multiple neural networks approach, with special architecture. A two-link robot example and its simulation results are presented to illustrate the proposed approach.

Online publication date: Wed, 17-Dec-2014

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Modelling, Identification and Control (IJMIC):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com