Name
Abstract | ||
|---|---|---|
A new adaptive state estimation algorithm, namely adaptive fading Kalman filter (AFKF), is proposed to solve the divergence problem of Kalman filter. A criterion function is constructed to measure the optimality of Kalman filter. The forgetting factor in AFKF is adaptively adjusted by minimizing the defined criterion function using measured outputs. The algorithm remains convergent and tends to be optimal in the presence of model errors. It has been successfully applied to the headbox of a paper-making machine for state estimation. |
Year | DOI | Venue |
|---|---|---|
1994 | 10.1016/0005-1098(94)90112-0 | Automatica |
Keywords | Field | DocType |
kalman filter,convergence,discrete system | Extended Kalman filter,Alpha beta filter,Fast Kalman filter,Control theory,Kernel adaptive filter,Adaptive filter,Ensemble Kalman filter,Invariant extended Kalman filter,Mathematics,Recursive least squares filter | Journal |
Volume | Issue | ISSN |
30 | 8 | 0005-1098 |
Citations | PageRank | References |
23 | 2.41 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qijun Xia | 1 | 23 | 2.41 |
| Ming Rao | 2 | 23 | 2.41 |
| Yiqun Ying | 3 | 23 | 2.41 |
| Xuemin Shen | 4 | 15389 | 928.67 |