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Applied system identification
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In this paper, the authors introduce the concept of Frequency Domain System ID (FDSI) and Frequency Response Functions (FRF) for time-domain models, as well as Frequency-Domain Models with Random Variables and Kalman Filter.Abstract:
1. Introduction. 2. Time-Domain Models. 3. Frequency-Domain Models. 4. Frequency Response Functions. 5. System Realization. 6. Observer Identification. 7. Frequency Domain System ID. 8. Observer/Controller ID. 9. Recursive Techniques. Appendix A: Fundamental Matrix Algebra. Appendix B: Random Variables and Kalman Filter. Appendix C: Data Acquisition.read more
Citations
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Bayesian Stochastic Neural Network Model for Turbomachinery Damage Prediction
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Book ChapterDOI
Designing Learning Control that is Close to Instability for Improved Parameter Identification
TL;DR: The first purpose of this paper is to develop modified ILC laws that are intentionally non-robust to model errors, as a way to fine tune the use of ILC for identification purposes, and the second purpose is to study the non-Robustness with respect to its ability to improve identification of system parameters when the model order is correct.
Journal ArticleDOI
Identification and Optimal Linear Tracking Control of ODU Autonomous Surface Vehicle
TL;DR: A novel approach of time-domain open-loop observer Kalman filter identifications (OKID) and state-feedback optimal linear tracking control of ODU-ASV is presented, in which a linear state-space model of ODV is obtained from the measured input and output data.
Stochastic parametric system identification approach for validation of finite element models: industrial applications
Azer A. Kasimzade,Sertac Tuhta +1 more
TL;DR: Stochastic parametric system parameters identifcation approach with taking into account the aliasing problem for validation of flnite element models is presented in this paper, where measurement noise perturbation in∞uences to the identifled system modal and physical pa- rameters.