Data-driven optimal terminal iterative learning control
This paper presents a data-driven optimal terminal iterative learning control (TILC) approach for linear and nonlinear discrete-time systems. The iterative learning control law is updated from only terminal output tracking error instead of entire output trajectory tracking error. The only required k...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/95852 http://hdl.handle.net/10220/11300 |
| _version_ | 1811684818113527808 |
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| author | Chi, Ronghu Wang, Danwei Hou, Zhongsheng Jin, Shangtai |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Chi, Ronghu Wang, Danwei Hou, Zhongsheng Jin, Shangtai |
| author_sort | Chi, Ronghu |
| collection | NTU |
| description | This paper presents a data-driven optimal terminal iterative learning control (TILC) approach for linear and nonlinear discrete-time systems. The iterative learning control law is updated from only terminal output tracking error instead of entire output trajectory tracking error. The only required knowledge of a controlled system is that the Markov matrices of linear systems or the partial derivatives of nonlinear systems with respect to control inputs are bounded. Rigorous analysis and convergence proof are developed with sufficient conditions for the terminal ILC design and the results are developed for both linear and nonlinear discrete-time systems. Simulation results illustrate the applicability and effectiveness of the proposed approach. |
| first_indexed | 2024-10-01T04:34:40Z |
| format | Journal Article |
| id | ntu-10356/95852 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:34:40Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/958522020-03-07T14:02:45Z Data-driven optimal terminal iterative learning control Chi, Ronghu Wang, Danwei Hou, Zhongsheng Jin, Shangtai School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering This paper presents a data-driven optimal terminal iterative learning control (TILC) approach for linear and nonlinear discrete-time systems. The iterative learning control law is updated from only terminal output tracking error instead of entire output trajectory tracking error. The only required knowledge of a controlled system is that the Markov matrices of linear systems or the partial derivatives of nonlinear systems with respect to control inputs are bounded. Rigorous analysis and convergence proof are developed with sufficient conditions for the terminal ILC design and the results are developed for both linear and nonlinear discrete-time systems. Simulation results illustrate the applicability and effectiveness of the proposed approach. 2013-07-12T04:03:54Z 2019-12-06T19:22:20Z 2013-07-12T04:03:54Z 2019-12-06T19:22:20Z 2012 2012 Journal Article Chi, R., Wang, D., Hou, Z., & Jin, S. (2012). Data-driven optimal terminal iterative learning control. Journal of Process Control, 22(10), 2026-2037. 0959-1524 https://hdl.handle.net/10356/95852 http://hdl.handle.net/10220/11300 10.1016/j.jprocont.2012.08.001 en Journal of process control © 2012 Elsevier Ltd. |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Chi, Ronghu Wang, Danwei Hou, Zhongsheng Jin, Shangtai Data-driven optimal terminal iterative learning control |
| title | Data-driven optimal terminal iterative learning control |
| title_full | Data-driven optimal terminal iterative learning control |
| title_fullStr | Data-driven optimal terminal iterative learning control |
| title_full_unstemmed | Data-driven optimal terminal iterative learning control |
| title_short | Data-driven optimal terminal iterative learning control |
| title_sort | data driven optimal terminal iterative learning control |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/95852 http://hdl.handle.net/10220/11300 |
| work_keys_str_mv | AT chironghu datadrivenoptimalterminaliterativelearningcontrol AT wangdanwei datadrivenoptimalterminaliterativelearningcontrol AT houzhongsheng datadrivenoptimalterminaliterativelearningcontrol AT jinshangtai datadrivenoptimalterminaliterativelearningcontrol |