Abstract
We present a frequency-domain subsystem identification algorithm that identifies unknown feedback and feedforward subsystems that are interconnected with a known subsystem. This method requires only accessible input and output measurements, applies to linear time-invariant subsystems, and uses a candidate-pool approach to ensure asymptotic stability of the identified closed-loop transfer function. We analyze the algorithm in the cases of noiseless and noisy data. The main analytic result of this paper shows that the coefficients of the identified feedback and feedforward transfer functions are arbitrarily close to the true coefficients if the data noise is sufficiently small and the candidate pool is sufficiently dense. This subsystem identification approach has application to modeling the control behavior of humans interacting with and receiving feedback from a dynamic system. We apply the algorithm to data from a human-in-the-loop experiment to model a human's control behavior.
Original language | English |
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Pages (from-to) | 36-46 |
Number of pages | 11 |
Journal | Systems and Control Letters |
Volume | 87 |
DOIs | |
State | Published - Jan 1 2016 |
Bibliographical note
Publisher Copyright:© 2015 Elsevier B.V.
Funding
This work is supported by the National Science Foundation (award number: CMMI–1405257 ) and the Kentucky Science and Engineering Foundation (award number: KSEF-148-502-12-288 ). We also want to acknowledge Dr. Sheetz and Mr. Gazula from the Center for Computational Sciences at the University of Kentucky for providing assistance in coding Algorithm 1 for parallel processing.
Funders | Funder number |
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National Science Foundation (NSF) | CMMI–1405257, 1405257 |
Kentucky Science and Engineering Foundation | KSEF-148-502-12-288 |
Keywords
- Human motor control
- Human-in-the-loop
- Subsystem identification
ASJC Scopus subject areas
- Control and Systems Engineering
- General Computer Science
- Mechanical Engineering
- Electrical and Electronic Engineering