Design of robust fuzzy iterative learning control for nonlinear batch processes
In this paper, a two-dimensional (2D) composite fuzzy iterative learning control (ILC) scheme for nonlinear batch processes is proposed. By employing the local-sector nonlinearity method, the nonlinear batch process is represented by a 2D uncertain T-S fuzzy model with non-repetitive disturbances. T...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
AIMS Press
2023-11-01
|
| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023897?viewType=HTML |
| _version_ | 1827594883770089472 |
|---|---|
| author | Wei Zou Yanxia Shen Lei Wang |
| author_facet | Wei Zou Yanxia Shen Lei Wang |
| author_sort | Wei Zou |
| collection | DOAJ |
| description | In this paper, a two-dimensional (2D) composite fuzzy iterative learning control (ILC) scheme for nonlinear batch processes is proposed. By employing the local-sector nonlinearity method, the nonlinear batch process is represented by a 2D uncertain T-S fuzzy model with non-repetitive disturbances. Then, the feedback control is integrated with the ILC scheme to be investigated under the constructed model. Sufficient conditions for robust asymptotic stability and 2D $ H_\infty $ performance requirements of the resulting closed-loop fuzzy system are established based on Lyapunov functions and some matrix transformation techniques. Furthermore, the corresponding controller gains can be derived from a set of linear matrix inequalities (LMIs). Finally, simulations on the three-tank system and the highly nonlinear continuous stirred tank reactor (CSTR) are carried out to prove the feasibility and efficiency of the proposed approach. |
| first_indexed | 2024-03-09T02:42:44Z |
| format | Article |
| id | doaj.art-dcd445aee0634b1988bc768b0d491f78 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-03-09T02:42:44Z |
| publishDate | 2023-11-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-dcd445aee0634b1988bc768b0d491f782023-12-06T01:18:34ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-11-012011202742029410.3934/mbe.2023897Design of robust fuzzy iterative learning control for nonlinear batch processesWei Zou0Yanxia Shen 1Lei Wang 21. Engineering Research Center of Internet of Things Technology Applications, Ministry of Education, Jiangnan University, Wuxi 214122, China1. Engineering Research Center of Internet of Things Technology Applications, Ministry of Education, Jiangnan University, Wuxi 214122, China2. School of Automation, Wuxi University, Wuxi 214105, ChinaIn this paper, a two-dimensional (2D) composite fuzzy iterative learning control (ILC) scheme for nonlinear batch processes is proposed. By employing the local-sector nonlinearity method, the nonlinear batch process is represented by a 2D uncertain T-S fuzzy model with non-repetitive disturbances. Then, the feedback control is integrated with the ILC scheme to be investigated under the constructed model. Sufficient conditions for robust asymptotic stability and 2D $ H_\infty $ performance requirements of the resulting closed-loop fuzzy system are established based on Lyapunov functions and some matrix transformation techniques. Furthermore, the corresponding controller gains can be derived from a set of linear matrix inequalities (LMIs). Finally, simulations on the three-tank system and the highly nonlinear continuous stirred tank reactor (CSTR) are carried out to prove the feasibility and efficiency of the proposed approach.https://www.aimspress.com/article/doi/10.3934/mbe.2023897?viewType=HTMLfuzzy iterative learning controlnonlinear batch processesuncertain t-s fuzzy modelrobust asymptotic stability2d $ h_\infty $ performance |
| spellingShingle | Wei Zou Yanxia Shen Lei Wang Design of robust fuzzy iterative learning control for nonlinear batch processes Mathematical Biosciences and Engineering fuzzy iterative learning control nonlinear batch processes uncertain t-s fuzzy model robust asymptotic stability 2d $ h_\infty $ performance |
| title | Design of robust fuzzy iterative learning control for nonlinear batch processes |
| title_full | Design of robust fuzzy iterative learning control for nonlinear batch processes |
| title_fullStr | Design of robust fuzzy iterative learning control for nonlinear batch processes |
| title_full_unstemmed | Design of robust fuzzy iterative learning control for nonlinear batch processes |
| title_short | Design of robust fuzzy iterative learning control for nonlinear batch processes |
| title_sort | design of robust fuzzy iterative learning control for nonlinear batch processes |
| topic | fuzzy iterative learning control nonlinear batch processes uncertain t-s fuzzy model robust asymptotic stability 2d $ h_\infty $ performance |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023897?viewType=HTML |
| work_keys_str_mv | AT weizou designofrobustfuzzyiterativelearningcontrolfornonlinearbatchprocesses AT yanxiashen designofrobustfuzzyiterativelearningcontrolfornonlinearbatchprocesses AT leiwang designofrobustfuzzyiterativelearningcontrolfornonlinearbatchprocesses |