Adaptive Critic Learning-Based Robust Control of Systems with Uncertain Dynamics
Model uncertainties are usually unavoidable in the control systems, which are caused by imperfect system modeling, disturbances, and nonsmooth dynamics. This paper presents a novel method to address the robust control problem for uncertain systems. The original robust control problem of the uncertai...
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Hindawi Limited
2021
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oai:doaj.org-article:9bc32b765165428aa6ad370e77de68b82021-11-29T00:55:42ZAdaptive Critic Learning-Based Robust Control of Systems with Uncertain Dynamics1687-527310.1155/2021/2952115https://doaj.org/article/9bc32b765165428aa6ad370e77de68b82021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/2952115https://doaj.org/toc/1687-5273Model uncertainties are usually unavoidable in the control systems, which are caused by imperfect system modeling, disturbances, and nonsmooth dynamics. This paper presents a novel method to address the robust control problem for uncertain systems. The original robust control problem of the uncertain system is first transformed into an optimal control of nominal system via selecting the appropriate cost function. Then, we develop an adaptive critic leaning algorithm to learn online the optimal control solution, where only the critic neural network (NN) is used, and the actor NN widely used in the existing methods is removed. Finally, the feasibility analysis of the control algorithm is given in the paper. Simulation results are given to show the availability of the presented control method.Jun ZhaoQingliang ZengBin GuoHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021) |
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Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 |
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Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Jun Zhao Qingliang Zeng Bin Guo Adaptive Critic Learning-Based Robust Control of Systems with Uncertain Dynamics |
description |
Model uncertainties are usually unavoidable in the control systems, which are caused by imperfect system modeling, disturbances, and nonsmooth dynamics. This paper presents a novel method to address the robust control problem for uncertain systems. The original robust control problem of the uncertain system is first transformed into an optimal control of nominal system via selecting the appropriate cost function. Then, we develop an adaptive critic leaning algorithm to learn online the optimal control solution, where only the critic neural network (NN) is used, and the actor NN widely used in the existing methods is removed. Finally, the feasibility analysis of the control algorithm is given in the paper. Simulation results are given to show the availability of the presented control method. |
format |
article |
author |
Jun Zhao Qingliang Zeng Bin Guo |
author_facet |
Jun Zhao Qingliang Zeng Bin Guo |
author_sort |
Jun Zhao |
title |
Adaptive Critic Learning-Based Robust Control of Systems with Uncertain Dynamics |
title_short |
Adaptive Critic Learning-Based Robust Control of Systems with Uncertain Dynamics |
title_full |
Adaptive Critic Learning-Based Robust Control of Systems with Uncertain Dynamics |
title_fullStr |
Adaptive Critic Learning-Based Robust Control of Systems with Uncertain Dynamics |
title_full_unstemmed |
Adaptive Critic Learning-Based Robust Control of Systems with Uncertain Dynamics |
title_sort |
adaptive critic learning-based robust control of systems with uncertain dynamics |
publisher |
Hindawi Limited |
publishDate |
2021 |
url |
https://doaj.org/article/9bc32b765165428aa6ad370e77de68b8 |
work_keys_str_mv |
AT junzhao adaptivecriticlearningbasedrobustcontrolofsystemswithuncertaindynamics AT qingliangzeng adaptivecriticlearningbasedrobustcontrolofsystemswithuncertaindynamics AT binguo adaptivecriticlearningbasedrobustcontrolofsystemswithuncertaindynamics |
_version_ |
1718407786121920512 |