Dynamic neural network-observer-based adaptive inverse optimal control design for unknown nonlinear systems Online publication date: Tue, 27-Oct-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.
Online publication date: Tue, 27-Oct-2015
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