- Title
- Data-driven modeling of a coupled electric drives system using regularized basis function Volterra kernels
- Creator
- Stoddard, Jeremy G.; Welsh, James S.
- Relation
- ICIRA: International Conference on Intelligent Robotics and Applications. Intelligent Robotics and Applications. ICIRA 2018 [Presented in Lecture Notes in Computer Science, vol 10984] (Newcastle, N.S.W 09-11 August, 2018) p. 475-485
- Publisher Link
- http://dx.doi.org/10.1007/978-3-319-97586-3_43
- Publisher
- Springer Nature
- Resource Type
- conference paper
- Date
- 2018
- Description
- In this paper, we consider the problem of data-driven modeling for systems containing nonlinear sensors. The issue is explored via an established nonlinear benchmark in the system identification community, referred to as the “coupled electric drives.” In the benchmark system, nonlinearity emerges in the pulse transducer used to measure the angular velocity of a pulley, which is invariant to the direction of rotation. In order to model the nonlinear dynamics without the use of extensive prior knowledge, we estimate a nonparametric Volterra series model using a regularized basis function approach. While the Volterra series is typically an impractical modeling tool due to the large number of parameters required, we obtain accurate models using only a short estimation dataset, by directly regularizing the basis function expansions of each Volterra kernel in a Bayesian framework.
- Subject
- system identification; Volterra series; regularization
- Identifier
- http://hdl.handle.net/1959.13/1392396
- Identifier
- uon:33396
- Identifier
- ISBN:9783319975856
- Rights
- "This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Computer Science. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-319-97586-3_43"
- Language
- eng
- Full Text
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