Open AccessNeuro observer‐based control of pure feedback MIMO systems with unknown control direction
Author(s) -
Ramezani Zahra,
Arefi Mohammad Mehdi,
Zargarzadeh Hassan,
JahedMotlagh Mohammad Reza
Publication year - 2017
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2016.0991
Subject(s) - control theory (sociology) , backstepping , observer (physics) , a priori and a posteriori , artificial neural network , bounded function , controller (irrigation) , adaptive control , mathematics , lyapunov function , mimo , computer science , nonlinear system , control (management) , artificial intelligence , physics , quantum mechanics , beamforming , mathematical analysis , philosophy , statistics , epistemology , agronomy , biology
This study focuses on the problem of neural network (NN)‐based tracking control for a class of uncertain multiple‐input multiple‐output non‐linear systems in pure feedback form. An observer based on K‐filters, is introduced to estimate immeasurable states. In this method, a priori knowledge of the control gain sign is relaxed by using Nussbaum‐type technique. NNs are employed to approximate the unknown non‐linear functions and an adaptive neural output feedback controller is constructed via backstepping technique. The Lyapunov theorem is applied to prove that the overall closed‐loop adaptive control scheme is semi‐globally uniformly ultimately bounded. Finally, simulation results are provided to illustrate the design procedure.