Name
Title | ||
|---|---|---|
Disturbance rejection via iterative learning control with a disturbance observer for active magnetic bearing systems. |
Abstract | ||
|---|---|---|
Although standard iterative learning control (ILC) approaches can achieve perfect tracking for active magnetic bearing (AMB) systems under external disturbances, the disturbances are required to be iteration-invariant. In contrast to existing approaches, we address the tracking control problem of AMB systems under iteration-variant disturbances that are in different channels from the control inputs. A disturbance observer based ILC scheme is proposed that consists of a universal extended state observer (ESO) and a classical ILC law. Using only output feedback, the proposed control approach estimates and attenuates the disturbances in every iteration. The convergence of the closed-loop system is guaranteed by analyzing the contraction behavior of the tracking error. Simulation and comparison studies demonstrate the superior tracking performance of the proposed control approach. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1631/FITEE.1800558 | Frontiers of Information Technology & Electronic Engineering |
Keywords | Field | DocType |
Active magnetic bearings (AMBs), Iterative learning control (ILC), Disturbance observer, TP27, TH133 | State observer,Convergence (routing),Control theory,Computer science,Communication channel,Magnetic bearing,Iterative learning control,Observer (quantum physics),Tracking error | Journal |
Volume | Issue | ISSN |
20 | 1 | 2095-9184 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ze-zhi Tang | 1 | 0 | 0.34 |
| Yuan-jin Yu | 2 | 0 | 0.34 |
| Zhenhong Li | 3 | 165 | 47.51 |
| Zhengtao Ding | 4 | 31 | 5.53 |