Title: Dynamic neural network-observer-based adaptive inverse optimal control design for unknown nonlinear systems

Authors: Ayman K. Alhejji; Mohammad R. Sayeh

Addresses: Department of Electrical Power Engineering Technology, Yanbu Industrial College, Yanbu Al-Sinayiah, P.O. Box 30436, Kingdom of Saudi Arabia ' Department of Electrical and Computer Engineering, Southern Illinois University, Carbondale, IL 62901, USA

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.

Keywords: adaptive control; inverse optimal control; dynamic neural networks; DNNs; controller design; unknown systems; nonlinear systems; neural network observers; feedback control; simulation.

DOI: 10.1504/IJIED.2015.072826

International Journal of Industrial Electronics and Drives, 2015 Vol.2 No.3, pp.203 - 212

Received: 08 Apr 2015
Accepted: 17 Aug 2015

Published online: 27 Oct 2015 *

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