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.
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 *