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 |
| Summary: | 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. |
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