Optimal H∞-Based Linear-Quadratic Regulator Tracking Control for Discrete-Time Takagi–Sugeno Fuzzy Systems With Preview Actions
Bibliographic record
Abstract
This paper investigates the optimal tracking control problem for discrete-time Takagi–Sugeno (T–S) systems. The control signal has three components: preview control for the previewable reference signal, integral control for the tracking error, and the state-feedback control for the plant. The optimization objective is a quadratic form of the tracking error and the control signal. By using the augmentation technique, the tracking controller design problem is converted into a design problem of the state-feedback controllers for augmented T–S fuzzy systems. The quadratic optimization objective is equivalent to the two-norm (in fact, the square of the two-norm) of a controlled output. Assuming that the external inputs of the augmented systems are l2 bounded, the H∞ performance index is employed to investigate and optimize the controller design. The controller gains can be obtained by solving a sequence of linear matrix inequalities (LMIs). An example on electromechanical system shows the efficacy of the proposed design method.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".