Adaptive Neural Network Control of a Class of Fractional Order Uncertain Nonlinear MIMO Systems with Input Constraints

Adaptive Neural Network Control of a Class of Fractional Order Uncertain Nonlinear MIMO Systems with Input Constraints

An adaptive backstepping control scheme for a class of incommensurate fractional order uncertain nonlinear multiple-input multiple-output (MIMO) systems subjected to constraints is discussed in this paper, which ensures the convergence of tracking errors even with dead-zone and saturation nonlineari...

Full description

Bibliographic Details
Main Authors: Changhui Wang, Mei Liang, Yongsheng Chai
Format: Article
Language:English
Published: Hindawi-Wiley 2019-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2019/1410278
id doaj-71ea9d6a19314480a6d65698bd738088
record_format Article
spelling doaj-71ea9d6a19314480a6d65698bd7380882020-11-25T00:11:16ZengHindawi-WileyComplexity1076-27871099-05262019-01-01201910.1155/2019/14102781410278Adaptive Neural Network Control of a Class of Fractional Order Uncertain Nonlinear MIMO Systems with Input ConstraintsChanghui Wang0Mei Liang1Yongsheng Chai2School of Electromechanical and Automotive Engineering, Yantai University, 32 Qingquan Road, Laishan District, Yantai, ChinaSchool of Electromechanical and Automotive Engineering, Yantai University, 32 Qingquan Road, Laishan District, Yantai, ChinaSchool of Electromechanical and Automotive Engineering, Yantai University, 32 Qingquan Road, Laishan District, Yantai, ChinaAn adaptive backstepping control scheme for a class of incommensurate fractional order uncertain nonlinear multiple-input multiple-output (MIMO) systems subjected to constraints is discussed in this paper, which ensures the convergence of tracking errors even with dead-zone and saturation nonlinearities in the controller input. Combined with backstepping and adaptive technique, the unknown nonlinear uncertainties are approximated by the radial basis function neural network (RBF NN) in each step of the backstepping procedure. Frequency distributed model of a fractional integrator and Lyapunov stability theory are used for ensuring asymptotic stability of the overall closed-loop system under input dead-zone and saturation. Moreover, the parameter update laws with incommensurate fractional order are used in the controller to compensate unknown nonlinearities. Two simulation results are presented at the end to ensure the efficacy of the proposed scheme.http://dx.doi.org/10.1155/2019/1410278
collection DOAJ
language English
format Article
sources DOAJ
author Changhui Wang
Mei Liang
Yongsheng Chai
spellingShingle Changhui Wang
Mei Liang
Yongsheng Chai
Adaptive Neural Network Control of a Class of Fractional Order Uncertain Nonlinear MIMO Systems with Input Constraints
Complexity
author_facet Changhui Wang
Mei Liang
Yongsheng Chai
author_sort Changhui Wang
title Adaptive Neural Network Control of a Class of Fractional Order Uncertain Nonlinear MIMO Systems with Input Constraints
title_short Adaptive Neural Network Control of a Class of Fractional Order Uncertain Nonlinear MIMO Systems with Input Constraints
title_full Adaptive Neural Network Control of a Class of Fractional Order Uncertain Nonlinear MIMO Systems with Input Constraints
title_fullStr Adaptive Neural Network Control of a Class of Fractional Order Uncertain Nonlinear MIMO Systems with Input Constraints
title_full_unstemmed Adaptive Neural Network Control of a Class of Fractional Order Uncertain Nonlinear MIMO Systems with Input Constraints
title_sort adaptive neural network control of a class of fractional order uncertain nonlinear mimo systems with input constraints
publisher Hindawi-Wiley
series Complexity
issn 1076-2787
1099-0526
publishDate 2019-01-01
description An adaptive backstepping control scheme for a class of incommensurate fractional order uncertain nonlinear multiple-input multiple-output (MIMO) systems subjected to constraints is discussed in this paper, which ensures the convergence of tracking errors even with dead-zone and saturation nonlinearities in the controller input. Combined with backstepping and adaptive technique, the unknown nonlinear uncertainties are approximated by the radial basis function neural network (RBF NN) in each step of the backstepping procedure. Frequency distributed model of a fractional integrator and Lyapunov stability theory are used for ensuring asymptotic stability of the overall closed-loop system under input dead-zone and saturation. Moreover, the parameter update laws with incommensurate fractional order are used in the controller to compensate unknown nonlinearities. Two simulation results are presented at the end to ensure the efficacy of the proposed scheme.
url http://dx.doi.org/10.1155/2019/1410278
work_keys_str_mv AT changhuiwang adaptiveneuralnetworkcontrolofaclassoffractionalorderuncertainnonlinearmimosystemswithinputconstraints
AT meiliang adaptiveneuralnetworkcontrolofaclassoffractionalorderuncertainnonlinearmimosystemswithinputconstraints
AT yongshengchai adaptiveneuralnetworkcontrolofaclassoffractionalorderuncertainnonlinearmimosystemswithinputconstraints
_version_ 1725404936026456064

Similar Items