Name
Abstract | ||
|---|---|---|
This paper presents a novel off-line iterative learning control algorithm for multiple-input- multiple-output time-varying discrete stochastic systems. Using the steady-state Kalman filtering method, we provide a novel framework for the selection of optimal/sub-optimal fixed learning gain matrices in real applications, which is convenient for engineers. Meanwhile, this framework considerably decreases the calculation about the operations of inverting matrix by introducing a matrix Riccati equation at every iteration. It is strictly proved that the input error covariance converges to its steady-state value asymptotically in the mean square sense, and accordingly, the tracking error covariance also converges. The numerical simulations verify the theoretical results. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2928673 | IEEE ACCESS |
Keywords | DocType | Volume |
Iterative learning control, steady-state Kalman filtering, sub-optimal fixed learning gain, matrix Riccati equation | Journal | 7 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tianbo Zhang | 1 | 0 | 0.34 |
| Dong Shen | 2 | 155 | 17.64 |
| Chen Liu | 3 | 34 | 26.22 |
| Xu Hongze | 4 | 6 | 3.66 |