Premium
Observer‐based adaptive neural control of uncertain MIMO nonlinear systems with unknown control direction
Author(s) -
Arefi Mohammad M.,
JahedMotlagh Mohammad R.
Publication year - 2013
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2347
Subject(s) - control theory (sociology) , nonlinear system , artificial neural network , mimo , observer (physics) , sign function , controller (irrigation) , lyapunov function , adaptive control , singularity , sign (mathematics) , computer science , mathematics , control (management) , artificial intelligence , channel (broadcasting) , computer network , mathematical analysis , physics , quantum mechanics , agronomy , biology
SUMMARY This paper investigates adaptive neural network output feedback control for a class of uncertain multi‐input multi‐output (MIMO) nonlinear systems with an unknown sign of control gain matrix. Because the system states are not required to be available for measurement, an observer is designed to estimate the system states. In order to deal with the unknown sign of control gain matrix, the Nussbaum‐type function is utilized. By using neural network, we approximated the unknown nonlinear functions and perfectly avoided the controller singularity problem. The stability of the closed‐loop system is analyzed by using Lyapunov method. Theoretical results are illustrated through a simulation example. Copyright © 2012 John Wiley & Sons, Ltd.