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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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 |
_version_ |
1721539529770270720 |