Adaptive Asymptotic Control for a Class of Uncertain Nonlinear Systems

This paper addresses the asymptotic tracking problem of adaptive neural control for a class of uncertain strict-feedback nonlinear systems. As a universal approximator, the neural network is widely utilized to solve the tracking control problem of unknown continuous nonlinear systems. Due to the exi...

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Main Authors: Hanqiao Huang, Shuangyu Dong, Zongcheng Liu, Renwei Zuo
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8753529/
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spelling doaj-379c9531b2264e188e474eb9a910fc632021-04-05T17:26:25ZengIEEEIEEE Access2169-35362019-01-017973659737310.1109/ACCESS.2019.29262648753529Adaptive Asymptotic Control for a Class of Uncertain Nonlinear SystemsHanqiao Huang0Shuangyu Dong1Zongcheng Liu2https://orcid.org/0000-0002-8291-8004Renwei Zuo3https://orcid.org/0000-0003-2465-4685Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an, ChinaSMZ Telecom Pty Ltd., Melbourne, VIC, AustraliaAeronautics Engineering College, Air Force Engineering University, Xi’an, ChinaAeronautics Engineering College, Air Force Engineering University, Xi’an, ChinaThis paper addresses the asymptotic tracking problem of adaptive neural control for a class of uncertain strict-feedback nonlinear systems. As a universal approximator, the neural network is widely utilized to solve the tracking control problem of unknown continuous nonlinear systems. Due to the existence of neural network approximation errors, previous neural network-based control approaches can only achieve the bounded tracking rather than the asymptotic tracking. This paper designs an asymptotic error eliminating term to achieve the adaptive neural asymptotic tracking. By utilizing the Lyapunov stability theory, all the variables of the resulting closed-loop system are proven to be semi-globally uniformly ultimately bounded, and the tracking error can converge to zero asymptotically by choosing design parameters appropriately. A simulation example is presented to show the effectiveness of the proposed control approach.https://ieeexplore.ieee.org/document/8753529/Asymptotic stabilityneural networkadaptive control
collection DOAJ
language English
format Article
sources DOAJ
author Hanqiao Huang
Shuangyu Dong
Zongcheng Liu
Renwei Zuo
spellingShingle Hanqiao Huang
Shuangyu Dong
Zongcheng Liu
Renwei Zuo
Adaptive Asymptotic Control for a Class of Uncertain Nonlinear Systems
IEEE Access
Asymptotic stability
neural network
adaptive control
author_facet Hanqiao Huang
Shuangyu Dong
Zongcheng Liu
Renwei Zuo
author_sort Hanqiao Huang
title Adaptive Asymptotic Control for a Class of Uncertain Nonlinear Systems
title_short Adaptive Asymptotic Control for a Class of Uncertain Nonlinear Systems
title_full Adaptive Asymptotic Control for a Class of Uncertain Nonlinear Systems
title_fullStr Adaptive Asymptotic Control for a Class of Uncertain Nonlinear Systems
title_full_unstemmed Adaptive Asymptotic Control for a Class of Uncertain Nonlinear Systems
title_sort adaptive asymptotic control for a class of uncertain nonlinear systems
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2019-01-01
description This paper addresses the asymptotic tracking problem of adaptive neural control for a class of uncertain strict-feedback nonlinear systems. As a universal approximator, the neural network is widely utilized to solve the tracking control problem of unknown continuous nonlinear systems. Due to the existence of neural network approximation errors, previous neural network-based control approaches can only achieve the bounded tracking rather than the asymptotic tracking. This paper designs an asymptotic error eliminating term to achieve the adaptive neural asymptotic tracking. By utilizing the Lyapunov stability theory, all the variables of the resulting closed-loop system are proven to be semi-globally uniformly ultimately bounded, and the tracking error can converge to zero asymptotically by choosing design parameters appropriately. A simulation example is presented to show the effectiveness of the proposed control approach.
topic Asymptotic stability
neural network
adaptive control
url https://ieeexplore.ieee.org/document/8753529/
work_keys_str_mv AT hanqiaohuang adaptiveasymptoticcontrolforaclassofuncertainnonlinearsystems
AT shuangyudong adaptiveasymptoticcontrolforaclassofuncertainnonlinearsystems
AT zongchengliu adaptiveasymptoticcontrolforaclassofuncertainnonlinearsystems
AT renweizuo adaptiveasymptoticcontrolforaclassofuncertainnonlinearsystems
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