Name
Abstract | ||
|---|---|---|
In this paper, a simple, yet novel method for state estimation and parameter identification for dynamic systems is presented. Apart from providing estimates of non-measurable state variables, the algorithm is also capable of estimating (constant) system parameters. The estimation algorithm is split in two parts. Firstly, an extended Kalman filter, whose state-space-model is augmented with quasi-linear expressions for parameter values, providing estimates for the state variables and the augmented parameter values. Secondly, a Monte-Carlo-fashioned approach, which identifies the rest of the parameter values that were not included in the augmentation of the state-space model. The Monte-Carlo-approach minimizes an objective function (the error between the measured and the estimated state variable). It is shown that the algorithm is capable of estimating the state- and parameter-values in a satisfying manner. The method is best applied offline and the theoretical developments will be demonstrated in case studies. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.ifacsc.2020.100103 | IFAC Journal of Systems and Control |
Keywords | DocType | Volume |
Parameter identification,Estimation,Kalman filter,System identification | Journal | 13 |
ISSN | Citations | PageRank |
2468-6018 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christoph Josef Backi | 1 | 1 | 1.83 |
| Jan Tommy Gravdahl | 2 | 327 | 43.60 |
| Sigurd Skogestad | 3 | 163 | 49.55 |