Dynamic neural network-observer-based adaptive inverse optimal control design for unknown nonlinear systems Online publication date: Tue, 03-Nov-2015
by Ayman K. Alhejji; Mohammad R. Sayeh
International Journal of Industrial Electronics and Drives (IJIED), Vol. 2, No. 3, 2015
Abstract: An observer-based adaptive inverse optimal control (AIOC) scheme is developed for unknown nonlinear systems. First, a dynamic neural network (DNN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed based on control Lyapunov function (CLF) via inverse optimal method to obtain the optimal feedback control. In this framework, a two-layer DNN is used to construct the observer which can be implemented to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics. The optimal control scheme is implemented to online control the unknown nonlinear systems and the DNN observer simultaneously. An asymptotic stability of the closed-loop system is guaranteed using Lyapunov-based analysis. Simulation results are presented to demonstrate the effectiveness of the proposed control scheme.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
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 Industrial Electronics and Drives (IJIED):
Login with your Inderscience username and 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
Our Blog 
Follow us on Twitter 
Visit us on Facebook