Name
Title | ||
|---|---|---|
Self-tuning strategy for a minimum variance control system of a highly disturbed process. |
Abstract | ||
|---|---|---|
The paper presents an original self-tuning strategy for a minimum variance controller used to control highly disturbed processes. The role of the proposed algorithm is to implement a self-protection mechanism against major disturbances produced by measurable perturbations acting on a controlled plant. By a variable setting of the control penalty factor (ρ), which weights the influence of the control variance in a cost criterion function, the main objective is to maintain the stability of the control system, even under action of high perturbations. This new self-tuning strategy is based on the assumption that the disturbances acting on the controlled plant are measurable, being implemented and tested for the case of an induction generator integrated into a wind energy conversion system. The power control system's main goal is to reject the disturbance generated by a mechanical torque variation, due to variable wind speed, acting as an external measurable perturbation. Depending on the variation range of this mechanical torque (small variations corresponding to a relatively stationary regime or high variations due to wind gusts), the proposed algorithm sets the control penalty factor to different values. As a result, the overall effect of the self-tuning strategy will be to extend the operating range of the wind aggregate, by maintaining the stability of the control system (strongly disturbed by high wind gusts) and also by an efficient disturbance rejection. As a novelty, the base rule of the self-tuning strategy proposes a rectangular pulse variation of the control penalty factor when a high disturbance occurs. By setting a high amplitude for this pulse variation of ρ, the control is strongly limited, thus ensuring the system stability. At the end of this pulse with a suitable width, the effect of disturbance is fast rejected by returning to a small stationary value of ρ, which allows high enough control. Although designed and validated for a particular case, this self-tuning strategy is generally valid for the minimum variance control of any other process disturbed by measurable perturbations. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.ejcon.2018.06.004 | European Journal of Control |
Keywords | Field | DocType |
Adaptive control,Minimum variance controller,Control penalty factor,Self-tuning strategy,Induction generator | Minimum-variance unbiased estimator,Control theory,Wind speed,Torque,Control theory,Self-tuning,Control system,Induction generator,Mathematics,Perturbation (astronomy) | Journal |
Volume | ISSN | Citations |
46 | 0947-3580 | 1 |
PageRank | References | Authors |
0.48 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ioan Filip | 1 | 25 | 18.11 |
| Cristian Vasar | 2 | 9 | 12.74 |
| Iosif Szeidert | 3 | 17 | 13.63 |
| Octavian Prostean | 4 | 29 | 21.20 |