Optimized decentralized control of large scale systems
Bibliographic record
Abstract
This paper presents a new optimal decentralized controller design method for solving the tracking and disturbance rejection problems for the class of large scale LTI systems, using only low order decentralized controllers. In particular, the class of large scale systems considered in this paper is described by the LTI model M2ÿ + M1y + M0y = Bu + Eω, where y is the output, u is the input, ω is an unknown constant disturbance, where e = y - yrefis the tracking error in the system and yrefis a constant given signal. To illustrate the type of results which can be obtained using the new optimal decentralized control design method, the control of a large flexible space structure is studied and compared with the standard centralized LQR-Observer controller. In this case it is shown that the new decentralized controller obtained is orders of magnitude lower in dimension than the standard centralized LQR-Observer controller. The proposed controller also has the property that if a sensor/actuator failure occurs, the resulting system has certain fail-safe properties. The proposed optimal controller also has the property that it is strongly robust as compared to the standard centralized LQR-Observer controller. In particular, the proposed controller can have the property of being some 5 orders of magnitude more robust than the standard LQR-Observer controller. An illustration of the new decentralized controller design method is applied to a large flexible space structure (LFSS) system with 5 inputs and 5 outputs and of order 24.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".