Unified linear time-invariant model predictive control for strong nonlinear chaotic systems
It is well known that an alone linear controller is difficult to control a chaotic system, because intensive nonlinearities exist in such system. Meanwhile, depending closely on a precise mathematical modeling of the system and high computational complexity, model predictive control has its inheren...
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doaj-2b184e67879d48d892d12a5e3844977b2020-11-25T02:03:26ZengVilnius University PressNonlinear Analysis1392-51132335-89632016-11-0121510.15388/NA.2016.5.2Unified linear time-invariant model predictive control for strong nonlinear chaotic systemsYuan Zhang0Mingwei Sun1Zengqiang Chen2Nankai University, ChinaNankai University, ChinaNankai University, China It is well known that an alone linear controller is difficult to control a chaotic system, because intensive nonlinearities exist in such system. Meanwhile, depending closely on a precise mathematical modeling of the system and high computational complexity, model predictive control has its inherent drawback in controlling nonlinear systems. In this paper, a unified linear time-invariant model predictive control for intensive nonlinear chaotic systems is presented. The presented model predictive control algorithm is based on an extended state observer, and the precise mathematical modeling is not required. Through this method, not only the required coefficient matrix of impulse response can be derived analytically, but also the future output prediction is explicitly calculated by only using the current output sample. Therefore, the computational complexity can be reduced sufficiently. The merits of this method include, the Diophantine equation needing no calculation, and independence of precise mathematical modeling. According to the variation of the cost function, the order of the controller can be reduced, and the system stability is enhanced. Finally, numerical simulations of three kinds of chaotic systems confirm the effectiveness of the proposed method. http://www.zurnalai.vu.lt/nonlinear-analysis/article/view/13444chaosextended state observerpredictive controlsynchronization |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yuan Zhang Mingwei Sun Zengqiang Chen |
spellingShingle |
Yuan Zhang Mingwei Sun Zengqiang Chen Unified linear time-invariant model predictive control for strong nonlinear chaotic systems Nonlinear Analysis chaos extended state observer predictive control synchronization |
author_facet |
Yuan Zhang Mingwei Sun Zengqiang Chen |
author_sort |
Yuan Zhang |
title |
Unified linear time-invariant model predictive control for strong nonlinear chaotic systems |
title_short |
Unified linear time-invariant model predictive control for strong nonlinear chaotic systems |
title_full |
Unified linear time-invariant model predictive control for strong nonlinear chaotic systems |
title_fullStr |
Unified linear time-invariant model predictive control for strong nonlinear chaotic systems |
title_full_unstemmed |
Unified linear time-invariant model predictive control for strong nonlinear chaotic systems |
title_sort |
unified linear time-invariant model predictive control for strong nonlinear chaotic systems |
publisher |
Vilnius University Press |
series |
Nonlinear Analysis |
issn |
1392-5113 2335-8963 |
publishDate |
2016-11-01 |
description |
It is well known that an alone linear controller is difficult to control a chaotic system, because intensive nonlinearities exist in such system. Meanwhile, depending closely on a precise mathematical modeling of the system and high computational complexity, model predictive control has its inherent drawback in controlling nonlinear systems. In this paper, a unified linear time-invariant model predictive control for intensive nonlinear chaotic systems is presented. The presented model predictive control algorithm is based on an extended state observer, and the precise mathematical modeling is not required. Through this method, not only the required coefficient matrix of impulse response can be derived analytically, but also the future output prediction is explicitly calculated by only using the current output sample. Therefore, the computational complexity can be reduced sufficiently. The merits of this method include, the Diophantine equation needing no calculation, and independence of precise mathematical modeling. According to the variation of the cost function, the order of the controller can be reduced, and the system stability is enhanced. Finally, numerical simulations of three kinds of chaotic systems confirm the effectiveness of the proposed method.
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topic |
chaos extended state observer predictive control synchronization |
url |
http://www.zurnalai.vu.lt/nonlinear-analysis/article/view/13444 |
work_keys_str_mv |
AT yuanzhang unifiedlineartimeinvariantmodelpredictivecontrolforstrongnonlinearchaoticsystems AT mingweisun unifiedlineartimeinvariantmodelpredictivecontrolforstrongnonlinearchaoticsystems AT zengqiangchen unifiedlineartimeinvariantmodelpredictivecontrolforstrongnonlinearchaoticsystems |
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1724948220822421504 |