Name
Abstract | ||
|---|---|---|
Establishing real-time models for electric motors is important when capturing authentic dynamic behavior of the motors to improve control performance, enhance robustness, and support diagnosis. Quantized sensors are less expensive, and remotely controlled motors mandate signal quantization. Such limitations on observations introduce challenging issues in motor parameter estimation. This paper develops estimators for model parameters of permanent-magnet direct current (PMDC) motors using quantized speed measurements. A typical linearized model structure of PMDC motors is used as a benchmark platform to demonstrate the technology and its key properties and benefits. Convergence properties are established. Simulations and experimental studies are performed to illustrate potential applications of the technology. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TVT.2013.2251431 | IEEE T. Vehicular Technology |
Keywords | Field | DocType |
DC motors,Mathematical model,Permanent magnet motors,Sensor systems,Convergence,Standards | Computer science,Control theory,Brushed DC electric motor,Electronic engineering,DC motor,Control engineering,Robustness (computer science),Estimation theory,Quantization (signal processing),System identification,Electric motor,Estimator | Journal |
Volume | Issue | ISSN |
62 | 7 | 0018-9545 |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammad A. Obeidat | 1 | 0 | 0.34 |
| Le Yi Wang | 2 | 792 | 87.63 |
| Feng Lin | 3 | 177 | 18.34 |