- Title
- An asymptotically optimal indirect approach to continuous-time system identification
- Creator
- González, Rodrigo A.; Rojas, Cristian R.; Welsh, James S.
- Relation
- 57th IEEE Conference on Decision and Control (CDC). Proceedings of the 57th IEEE Conference on Decision and Control (Miami Beach, FL 17-19 December, 2018) p. 638-643
- Publisher Link
- http://dx.doi.org/10.1109/CDC.2018.8619141
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2018
- Description
- The indirect approach to continuous-time system identification consists in estimating continuous-time models by first determining an appropriate discrete-time model. For a zero-order hold sampling mechanism, this approach usually leads to a transfer function estimate with relative degree 1, independent of the relative degree of the strictly proper real system. In this paper, a refinement of these methods is developed. Inspired by the indirect prediction error method, we propose an estimator that enforces a fixed relative degree in the continuous-time transfer function estimate, and show that the estimator is consistent and asymptotically efficient. Extensive numerical simulations are put forward to show the performance of this estimator when contrasted with other indirect and direct methods for continuous-time system identification.
- Subject
- transfer functions; data models; mathematical model; maximum likelihood estimation; covariance matrices; numerical models; predictive models
- Identifier
- http://hdl.handle.net/1959.13/1404441
- Identifier
- uon:35341
- Identifier
- ISBN:978153861395
- Rights
- © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
- Language
- eng
- Full Text
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