The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Identification of Linear Time-Varying Systems: Kalman Filter Approach
|
|
Author(s): Vinayak G. Asutkar (Shri Guru Gobind Singhji Institute of Engineering and Technology, India)and Balasaheb M. Patre (Shri Guru Gobind Singhji Institute of Engineering and Technology, India)
Copyright: 2010
Pages: 17
Source title:
Intelligent Industrial Systems: Modeling, Automation and Adaptive Behavior
Source Author(s)/Editor(s): Gerasimos Rigatos (Industrial Systems Institute & National Technical University of Athens, Greece)
DOI: 10.4018/978-1-61520-849-4.ch008
Purchase
|
Abstract
This chapter deals with identification of time-varying systems using Kalman filter approach. Most physical systems exhibit some degree of time-varying behaviour for many reasons. These systems cannot effectively be modelled using time invariant models. A time-varying autoregressive with exogenous input (TVARX) model is good to model these time-varying systems. The Kalman filter approach is a superior way to estimate the system parameters. This approach can track the time-varying parameters and is suitable for recursive estimation. It works well even when there are abrupt changes in the system parameters. Kalman filter is known to be an optimal estimator even when there is significant noise. In the proposed approach, for the purpose of simulation, we employ first order TVARX model and its parameters are estimated using recursive Kalman filter method. The system parameters are varied in continuous and abruptly changing manner to reveal the physical situation. To show the efficacy of the proposed approach, the time-varying parameters are estimated for different noise conditions. The performance is evaluated by calculating error performance measures. The results are found to be satisfactory with reasonable accuracy for noisy conditions even for fast changing parameters. The numerical examples illustrate efficacy of the proposed Kalman filter based approach for identification of time-varying systems.
Related Content
|
Poshan Yu, Zixuan Zhao, Emanuela Hanes.
© 2023.
29 pages.
|
|
Subramaniam Meenakshi Sundaram, Tejaswini R. Murgod, Madhu M. Nayak, Usha Rani Janardhan, Usha Obalanarasimhaiah.
© 2023.
20 pages.
|
|
Rekha R. Nair, Tina Babu, Kishore S..
© 2023.
23 pages.
|
|
Wasswa Shafik.
© 2023.
22 pages.
|
|
Jay Kumar Jain, Dipti Chauhan.
© 2023.
24 pages.
|
|
George Makropoulos, Dimitrios Fragkos, Harilaos Koumaras, Nancy Alonistioti, Alexandros Kaloxylos, Vaios Koumaras, Theoni Dounia, Christos Sakkas, Dimitris Tsolkas.
© 2023.
19 pages.
|
|
Shouvik Sanyal, Kalimuthu M., Thangaraja Arumugam, Aruna R., Balaji J., Ajitha Savarimuthu, Chandan Chavadi, Dhanabalan Thangam, Sendhilkumar Manoharan, Shasikala Patil.
© 2023.
17 pages.
|
|
|