State-delay estimation for nonlinear systems using inexact output data
This paper considers the problem of using inexact output data to estimate the values of unknown state-delays in a general nonlinear time-delay system. We formulate the problem as a nonlinear optimization problem in which the state-delays are decision parameters and the cost function penalizes a weig...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE
2014
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/32780 |
| _version_ | 1848753758621663232 |
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| author | Lin, Qun Loxton, Ryan Xu, C. Teo, Kok Lay |
| author2 | Shengyuan Xu |
| author_facet | Shengyuan Xu Lin, Qun Loxton, Ryan Xu, C. Teo, Kok Lay |
| author_sort | Lin, Qun |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper considers the problem of using inexact output data to estimate the values of unknown state-delays in a general nonlinear time-delay system. We formulate the problem as a nonlinear optimization problem in which the state-delays are decision parameters and the cost function penalizes a weighted sum of the mean and variance of the least-squares error between actual and predicted system output. Our main result shows that the gradient of the least-squares cost function can be computed by solving an auxiliary time-advance system backward in time. On this basis, the state-delay estimation problem can be solved efficiently using standard gradient-based optimization algorithms such as sequential quadratic programming. We conclude the paper by testing this approach on a dynamic model of a continuously-stirred tank reactor with recycle loop. |
| first_indexed | 2025-11-14T08:29:36Z |
| format | Conference Paper |
| id | curtin-20.500.11937-32780 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:29:36Z |
| publishDate | 2014 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-327802023-02-27T07:34:31Z State-delay estimation for nonlinear systems using inexact output data Lin, Qun Loxton, Ryan Xu, C. Teo, Kok Lay Shengyuan Xu Qianchuan Zhao Nonlinear system Parameter estimation Time-delay system Gradient-based optimization This paper considers the problem of using inexact output data to estimate the values of unknown state-delays in a general nonlinear time-delay system. We formulate the problem as a nonlinear optimization problem in which the state-delays are decision parameters and the cost function penalizes a weighted sum of the mean and variance of the least-squares error between actual and predicted system output. Our main result shows that the gradient of the least-squares cost function can be computed by solving an auxiliary time-advance system backward in time. On this basis, the state-delay estimation problem can be solved efficiently using standard gradient-based optimization algorithms such as sequential quadratic programming. We conclude the paper by testing this approach on a dynamic model of a continuously-stirred tank reactor with recycle loop. 2014 Conference Paper http://hdl.handle.net/20.500.11937/32780 IEEE fulltext |
| spellingShingle | Nonlinear system Parameter estimation Time-delay system Gradient-based optimization Lin, Qun Loxton, Ryan Xu, C. Teo, Kok Lay State-delay estimation for nonlinear systems using inexact output data |
| title | State-delay estimation for nonlinear systems using inexact output data |
| title_full | State-delay estimation for nonlinear systems using inexact output data |
| title_fullStr | State-delay estimation for nonlinear systems using inexact output data |
| title_full_unstemmed | State-delay estimation for nonlinear systems using inexact output data |
| title_short | State-delay estimation for nonlinear systems using inexact output data |
| title_sort | state-delay estimation for nonlinear systems using inexact output data |
| topic | Nonlinear system Parameter estimation Time-delay system Gradient-based optimization |
| url | http://hdl.handle.net/20.500.11937/32780 |