Robust LQG Controller Design by LMI Approach of a Doubly-Fed Induction Generator for Aero-Generator
Bibliographic record
Abstract
This article presents a design method to improve the robustness in stability and performance of an LQG controller by the LMI approach applied to a multivariable system subject to parametric uncertainties, where its variations are known to have a direct impact on the degradation of the robustness margins of a classical LQG controller.The main of this work is to synthesize a robust Linear Quadratic Gaussian (LQG) controller reformulated by the Linear Matrix Inequality (LMI) approach and to apply it on an illconditioned system.Our choice fell on a doubly fed induction generator (DFIG) of the aero-generator to produce electrical energy, whose physical parameters are uncertain due to several factors: winding heating, magnetic saturation..., this makes it difficult to maintain the voltage at 220V and the frequency at 50Hz.First, the mathematical model of DFIG is written in a d-q reference frame.The singular values of the uncertainties are quantified and multiplied at the system output, and then the robustness conditions are determined.Secondly, the robust control law by the LQG synthesis based on the solution of the convex optimization problem under LMI Eigenvalue problem is elaborated and detailed.The simulation results of the stability and performance robustness of the LQG controller by the LMI approach with nominal and disturbed model of the DFIG are presented and discussed on the method efficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".