Accurate state estimates are required for increasingly complex systems, to enable, for example, feedback control. However, available state estimation schemes are not necessarily real-time feasible for certain large-scale systems. Therefore, we develop in this paper, a real-time feasible state-estimation scheme for a class of large-scale systems that approximates the steady state Kalman filter. In particular, we focus on systems where the state-vector is the result of discretizing the spatial domain, as typically seen in Partial Differential Equations. In such cases, the correlation between states in the state-vector often have an intuitive interpretation on the spatial domain, which can be exploited to obtain a significant reduction in computational complexity, while still providing accurate state estimates. We illustrate these strengths of our method through a hyperthermia cancer treatment case study. The results of the case study show significant improvements in the computation time, while simultaneously obtaining good state estimates, when compared to Ensemble Kalman filters and Kalman filters using reduced-order models.
|Title of host publication||2022 IEEE 61st Conference on Decision and Control, CDC 2022|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - 9 Dec 2022|
|Event||61st IEEE Conference on Decision and Control, CDC 2022 - Cancun, Mexico|
Duration: 6 Dec 2022 → 9 Dec 2022
|Series||Proceedings of the IEEE Conference on Decision and Control|
|Conference||61st IEEE Conference on Decision and Control, CDC 2022|
|Period||6/12/22 → 9/12/22|
Bibliographical noteFunding Information:
This research is supported by KWF Kankerbestrijding and NWO Domain AES, as part of their joint strategic research programme: Technology for Oncology II. The collaboration project is co-funded by the PPP Allowance made available by Health∼Holland, Top Sector Life Sciences & Health, to stimulate public-private partnerships.
Publisher Copyright: © 2022 IEEE.