Abstract
An important practical consideration in system identification is the judicious use of information for input signal design. Typically, only limited process knowledge is available a priori; hence the input design parameters are not always optimally selected. The quality of the data substantially improves if input design parameters can be refined during experimental execution. The purpose of this paper is to present an enhanced identification test monitoring procedure for multivariable systems that incorporates these ideas to achieve experiments with the shortest possible duration and that are adequately informative for identification purposes. This is enabled by 'on-the-go' manipulation of amplitude, duration, and/or frequency content of the input signals. The decision to continue, modify, or halt the experiment is achieved by a stopping criterion based on a robust control metric that is developed in the paper. These computations are performed using an orthogonal-in-frequency spectral input design, and relying on a computational method that estimates transfer functions (and associated uncertainties) taking into account the system noise and transient behavior. Results are evaluated through a simulation study using a chemical process system under diverse realistic noise structures.
Original language | English (US) |
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Title of host publication | 2016 IEEE 55th Conference on Decision and Control, CDC 2016 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 2091-2096 |
Number of pages | 6 |
ISBN (Electronic) | 9781509018376 |
DOIs | |
State | Published - Dec 27 2016 |
Event | 55th IEEE Conference on Decision and Control, CDC 2016 - Las Vegas, United States Duration: Dec 12 2016 → Dec 14 2016 |
Other
Other | 55th IEEE Conference on Decision and Control, CDC 2016 |
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Country | United States |
City | Las Vegas |
Period | 12/12/16 → 12/14/16 |
ASJC Scopus subject areas
- Artificial Intelligence
- Decision Sciences (miscellaneous)
- Control and Optimization