Composite fuzzy learning finite-time prescribed performance control of uncertain nonlinear systems with dead-zone inputs
This paper presents a composite fuzzy learning finite-time prescribed performance control (PPC) approach for uncertain nonlinear systems with dead-zone inputs. First, a finite-time performance function is constructed by a quartic polynomial. Subsequently, with the help of an error transformation fun...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Applied Mathematics and Statistics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fams.2022.1041588/full |
| _version_ | 1811257549755777024 |
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| author | Fang Zhu Fang Zhu Pengtong Li |
| author_facet | Fang Zhu Fang Zhu Pengtong Li |
| author_sort | Fang Zhu |
| collection | DOAJ |
| description | This paper presents a composite fuzzy learning finite-time prescribed performance control (PPC) approach for uncertain nonlinear systems with dead-zone inputs. First, a finite-time performance function is constructed by a quartic polynomial. Subsequently, with the help of an error transformation function, the restriction problem of the tracking error performance is transformed into a stability problem of an equivalent transformation system. In order to ensure that all signals of the closed-loop system are bounded, a finite-time PPC method combined with fuzzy logic systems (FLSs) is proposed. Although the tracking error can be guaranteed to be limited within a predefined range, the proposed finite-time PPC method only uses instantaneous data, which cannot guarantee the accurate estimation of unknown functions under the influence of dead-zone inputs. Therefore, based on the persistent excitation (PE) condition, a predictive error is defined by using online recorded data and instantaneous data, and a corresponding composite learning finite-time PPC method with parameter updating the law, which can not only achieve the control aim of the former method but also improve the control effect, is designed. The simulation results verified that the composite learning finite-time PPC method is more effective than the finite-time PPC method without learning. |
| first_indexed | 2024-04-12T17:59:09Z |
| format | Article |
| id | doaj.art-32c421cf43f1461ca9862a64364a8646 |
| institution | Directory Open Access Journal |
| issn | 2297-4687 |
| language | English |
| last_indexed | 2024-04-12T17:59:09Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Applied Mathematics and Statistics |
| spelling | doaj.art-32c421cf43f1461ca9862a64364a86462022-12-22T03:22:14ZengFrontiers Media S.A.Frontiers in Applied Mathematics and Statistics2297-46872022-10-01810.3389/fams.2022.10415881041588Composite fuzzy learning finite-time prescribed performance control of uncertain nonlinear systems with dead-zone inputsFang Zhu0Fang Zhu1Pengtong Li2College of Mathematics, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaSchool of Finance and Mathematics, Huainan Normal University, Huainan, ChinaCollege of Mathematics, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaThis paper presents a composite fuzzy learning finite-time prescribed performance control (PPC) approach for uncertain nonlinear systems with dead-zone inputs. First, a finite-time performance function is constructed by a quartic polynomial. Subsequently, with the help of an error transformation function, the restriction problem of the tracking error performance is transformed into a stability problem of an equivalent transformation system. In order to ensure that all signals of the closed-loop system are bounded, a finite-time PPC method combined with fuzzy logic systems (FLSs) is proposed. Although the tracking error can be guaranteed to be limited within a predefined range, the proposed finite-time PPC method only uses instantaneous data, which cannot guarantee the accurate estimation of unknown functions under the influence of dead-zone inputs. Therefore, based on the persistent excitation (PE) condition, a predictive error is defined by using online recorded data and instantaneous data, and a corresponding composite learning finite-time PPC method with parameter updating the law, which can not only achieve the control aim of the former method but also improve the control effect, is designed. The simulation results verified that the composite learning finite-time PPC method is more effective than the finite-time PPC method without learning.https://www.frontiersin.org/articles/10.3389/fams.2022.1041588/fullnonlinear systemperformance functionpartial persistent excitationdead-zone inputfinite-time |
| spellingShingle | Fang Zhu Fang Zhu Pengtong Li Composite fuzzy learning finite-time prescribed performance control of uncertain nonlinear systems with dead-zone inputs Frontiers in Applied Mathematics and Statistics nonlinear system performance function partial persistent excitation dead-zone input finite-time |
| title | Composite fuzzy learning finite-time prescribed performance control of uncertain nonlinear systems with dead-zone inputs |
| title_full | Composite fuzzy learning finite-time prescribed performance control of uncertain nonlinear systems with dead-zone inputs |
| title_fullStr | Composite fuzzy learning finite-time prescribed performance control of uncertain nonlinear systems with dead-zone inputs |
| title_full_unstemmed | Composite fuzzy learning finite-time prescribed performance control of uncertain nonlinear systems with dead-zone inputs |
| title_short | Composite fuzzy learning finite-time prescribed performance control of uncertain nonlinear systems with dead-zone inputs |
| title_sort | composite fuzzy learning finite time prescribed performance control of uncertain nonlinear systems with dead zone inputs |
| topic | nonlinear system performance function partial persistent excitation dead-zone input finite-time |
| url | https://www.frontiersin.org/articles/10.3389/fams.2022.1041588/full |
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