Algoritma Robust Kalman Filtering untuk Sistem Waktu Kontinu yang Tidak Pasti


DOI: https://doi.org/10.30998/.v3i1.3045

Budi Rudianto(1*), Muhafzan Muhafzan(2), Mahdivan Syafwann(3), Syafrizal Sy(4),

(1) Departemen Matematika dan Sains Data Universitas Andalas
(2) 
(3) 
(4) 
(*) Corresponding Author

Abstract


The Kalman filtering algorithm is an estimation method widely used in various engineering applications, such as navigation, control, and communication systems. However, the performance of this algorithm can degrade drastically when applied to systems with model uncertainty and disturbances. This paper discusses the construction and numerical simulation of a Robust Kalman Filtering Algorithm that is able to cope with uncertainties in continuous time systems. This algorithm shows better performance compared to conventional Kalman Filtering under uncertain conditions.

Full Text:

PDF

References


Chukhrova, N., & Johannssen, A. (2021). Kalman filter learning algorithms and state space representations for stochastic claims reserving. Risks, 9(6). https://doi.org/10.3390/risks9060112 Daid, A., Busvelle, E., & Aidene, M. (2021). On the convergence of the unscented Kalman filter. European Journal of Control, 57. https://doi.org/10.1016/j.ejcon.2020.05.003 Ghion, D., & Zorzi, M. (2022). Distributed Kalman filtering with event-Triggered communication: A robust approach. 2022 30th Mediterranean Conference on Control and Automation, MED 2022, 1, 785–790. https://doi.org/10.1109/MED54222.2022.9837137 Han, T., Gois, F. N. B., Oliveira, R., Prates, L. R., & Porto, M. M. de A. (2023). Modeling the progression of COVID-19 deaths using Kalman Filter and AutoML. Soft Computing, 27(6). https://doi.org/10.1007/s00500-020-05503-5 Hao, N., He, F., Tian, C., Yao, Y., & Xia, W. (2023). KD-EKF: A Consistent Cooperative Localization Estimator Based on Kalman Decomposition. IEEE International Conference on Intelligent Robots and Systems, Cl, 11064–11070. https://doi.org/10.1109/IROS55552.2023.10341604 Kim, S., Deshpande, V. M., & Bhattacharya, R. (2021). Robust Kalman Filtering with Probabilistic Uncertainty in System Parameters. IEEE Control Systems Letters, 5(1), 295–300. https://doi.org/10.1109/LCSYS.2020.3001490 Liu, B. (2010). Uncertain Set Theory and Uncertain Inference Rule with Application to Uncertain Control. Online, 4(2), 83–98. Liu, Z., Chen, Y., & Lu, Y. (2022). Mid-State Kalman Filter for Nonlinear Problems. Sensors, 22(4). https://doi.org/10.3390/s22041302 Mary, A. H., Miry, A. H., & Miry, M. H. (2021). System uncertainties estimation based adaptive robust backstepping control for DC DC buck converter. International Journal of Electrical and Computer Engineering, 11(1), 347–355. https://doi.org/10.11591/ijece.v11i1.pp347-355 Meyer, G. P. (2021). An Alternative Probabilistic Interpretation of the Huber Loss. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Section 2, 5257–5265. https://doi.org/10.1109/CVPR46437.2021.00522 Wang, R., Becker, D., & Hobiger, T. (2023). Stochastic modeling with robust Kalman filter for real-time kinematic GPS single-frequency positioning. GPS Solutions, 27(3), 1–17. https://doi.org/10.1007/s10291-023-01479-5 Yi, S., & Zorzi, M. (2022). Robust Kalman Filtering Under Model Uncertainty: The Case of Degenerate Densities. IEEE Transactions on Automatic Control, 67(7), 3458–3471. https://doi.org/10.1109/TAC.2021.3106861




DOI: https://doi.org/10.30998/.v3i1.3045

Article Metrics

Abstract Views : 173 | PDF Views : 52

Refbacks

  • There are currently no refbacks.