Optimal H∞-Based Linear-Quadratic Regulator Tracking Control for Discrete-Time Takagi–Sugeno Fuzzy Systems With Preview Actions
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
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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 it