Neuro observer‐based control of pure feedback MIMO systems with unknown control direction. Issue 2 (1st January 2017)

• Record Type: Journal Article
• Authors: Ramezani, Zahra; Arefi, Mohammad Mehdi; Zargarzadeh, Hassan; Jahed‐Motlagh, Mohammad Reza
• Is Part Of: IET control theory & applications. Volume 11:Issue 2(2017)
• Page Start: 213
• Page End: 224
• Publication Date: 2017-01-01
• Subjects: neurocontrollers -- observers -- feedback -- MIMO systems -- uncertain systems -- nonlinear systems -- adaptive control -- control nonlinearities -- Lyapunov methods -- closed loop systems; neuro observer‐based control -- pure feedback MIMO systems -- unknown control direction -- neural network -- NN‐based tracking control -- uncertain multiple‐input multiple‐output nonlinear systems -- K‐filters -- immeasurable states -- control gain sign -- Nussbaum‐type technique -- unknown nonlinear functions -- adaptive neural output feedback controller -- backstepping technique -- Lyapunov theorem -- closed loop adaptive control; Control theory -- Periodicals; Automatic control -- Periodicals; 629.8312
• DOI: 10.1049/iet-cta.2016.0991
• Languages: English
• ISSNs: 1751-8644

Abstract

Abstract : 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.