Application of a Modified Joint Unscented Kalman Filter for Parameter Estimation of a Class of Mechanical Systems

Yıl 2024, Cilt: 14 Sayı: 4, 2338 - 2357, 15.12.2024

Altan Onat

https://doi.org/10.31466/kfbd.1561543

Öz

The unscented Kalman filter (UKF) is a tool for state and parameter estimation in nonlinear dynamical systems. There are two primary approaches for utilizing UKF in parameter and state estimation. The first approach is known as joint filtering, where both states and parameters are estimated concurrently. The second approach, dual filtering, involves the use of two separate filters to estimate states and parameters independently. Recently, a modified joint Unscented Kalman Filter (MJUKF) has been introduced by the author. This modified approach is applicable to a class of nonlinear systems and offers two significant advantages which are improved estimation accuracy and reduced computational complexity. This study investigates the application of the MJUKF for parameter estimation in two selected mechanical systems. The results demonstrate that the modified filter effectively estimates parameters in mechanical systems revealing its advantages over the standard joint unscented Kalman filtering method in terms of both accuracy and computational complexity.

Anahtar Kelimeler

Unscented Kalman filter, Parameter estimation, Joint unscented Kalman filter, Coupled tank, Single link flexible joint

Değiştirilmiş Kokusuz Kalman Filtresinin Parametre Tahmini için Bir Mekanik Sistem Sınıfına Uygulanışı

Öz

Kokusuz Kalman filtresi (KKF), doğrusal olmayan dinamik sistemlerde durum ve parametre tahmini için kullanılan bir araçtır. KKF'nin durum ve parametre tahmininde kullanılmasında iki ana yaklaşım bulunmaktadır. İlk yaklaşım hem durumların hem de parametrelerin eşzamanlı olarak tahmin edildiği birleşik filtreleme olarak bilinir. İkinci yaklaşım ise durum ve parametrelerin bağımsız olarak tahmin edildiği iki ayrı filtrenin kullanıldığı ikili filtreleme yöntemidir. Yakın zamanda, yazar tarafından Değiştirilmiş Birleşik Kokusuz Kalman Filtresi (DBKKF) tanıtılmıştır. Bu değiştirilmiş yaklaşım, doğrusal olmayan sistemlerin bir sınıfına uygulanabilir ve iki önemli avantaj sunar: iyileştirilmiş tahmin doğruluğu ve azaltılmış hesaplama karmaşıklığı. Bu çalışma, DBKKF'nin iki seçilmiş mekanik sistemde parametre tahmini için uygulanmasını incelemektedir. Sonuçlar, değiştirilmiş filtrenin mekanik sistemlerde parametreleri etkin bir şekilde tahmin ettiğini ve hem doğruluk hem de hesaplama karmaşıklığı açısından standart birleşik kokusuz Kalman filtreleme yöntemine göre avantajlarını ortaya koymaktadır.

Anahtar Kelimeler

Kokusuz Kalman Filtresi, Parametre tahmini, Birleşik kokusuz Kalman filtresi, Bağlı Tank, Tek bağlantılı esnek eklem

Kaynakça

• González-Cagigal, M. A., Rosendo-Macı́as, J. A., & Gómez-Expósito, A. (2019). Parameter estimation of fully regulated synchronous generators using unscented Kalman filters. Electric power systems research, 168, 210–217.
• González-Cagigal, M. Á., Rosendo-Macı́as, J. A., & Gómez-Expósito, A. (2022). State and Parameter Estimation of Photovoltaic Modules using Unscented Kalman Filters. RE&PQJ, 20.
• Gonzólez-Cagigal, M. Á., Rosendo-Macı́as, J. A., & Gómez-Expósito, A. (2022). Parameter estimation for hot-spot thermal model of power transformers using unscented Kalman filters. Journal of Modern Power Systems and Clean Energy, 11, 634–642.
• HP. (2024, April 01). HP Z420 Workstation Product Specifications. Retrieved from https://support.hp.com/us-en/document/c03277050
• Onat, A. (2019). A novel and computationally efficient joint unscented Kalman filtering scheme for parameter estimation of a class of nonlinear systems. Ieee Access, 7, 31634–31655.
• Onat, A. (2020). Source Code: A Novel and Computationally Efficient Joint Unscented Kalman Filtering Scheme for Parameter Estimation of a Class of Nonlinear Systems. Retrieved from https://github.com/altanonat/MJUKF
• Onat, A. (2024). Source Code: Application of a Modified Joint Unscented Kalman Filter for Parameter Estimation of a Class of Mechanical Systems. Retrieved from Github: https://github.com/altanonat/mjukf-application
• Onat, A., & Kayaalp, B. T. (2020). A joint unscented Kalman filter-based dynamic weigh in motion system for railway vehicles with traction. IEEE Sensors Journal, 21, 15709–15718.
• Onat, A., & Örüklü, K. (2023). A New Numerical Approach for Simulation of Power Electronics Converters. 14th International Conference on electrical and Electronics Engineering (ELECO) (pp. 1-5). IEEE.
• Onat, A., Voltr, P., & Lata, M. (2018). An unscented Kalman filter-based rolling radius estimation methodology for railway vehicles with traction. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 232, 1686–1702.
• Pei, X., Chen, Z., Yang, B., & Chu, D. (2020). Estimation of states and parameters of multi-axle distributed electric vehicle based on dual unscented Kalman filter. Science progress, 103, 0036850419880083.
• Peng, N., Zhang, S., Guo, X., & Zhang, X. (2021). Online parameters identification and state of charge estimation for lithium-ion batteries using improved adaptive dual unscented Kalman filter. International Journal of Energy Research, 45, 975–990.
• Raghavan, S., & Hedrick, J. K. (1994). Observer design for a class of nonlinear systems. International Journal of Control, 59, 515–528.
• Seung, J.-H., Atiya, A. F., Parlos, A. G., & Chong, K.-T. (2017). Identification of unknown parameter value for precise flow control of Coupled Tank using Robust Unscented Kalman filter. International Journal of Precision Engineering and Manufacturing, 18, 31–38.
• Uysal, C., Onat, A., & Filik, T. (2020). Non-contact respiratory rate estimation in real-time with modified joint unscented Kalman filter. Ieee Access, 8, 99445–99457.
• Van Der Merwe, R. (2004). Sigma-point Kalman filters for probabilistic inference in dynamic state-space models. Oregon Health & Science University.