PWATOOLS: A MATLAB toolbox for piecewise-affine controller synthesis
Bibliographic record
Abstract
A toolbox for piecewise-affine (PWA) systems called PWATOOLS is introduced in this paper. Numerical control synthesis methodologies for PWA and nonlinear systems have been implemented in this toolbox. Although several Lyapunov-based PWA control synthesis approaches exist in the literature, to the best of our knowledge there is no software toolbox that implements these methods for continuous-time PWA systems that is also capable of analyzing nonlinear systems and synthesizing PWA controllers for them. PWATOOLS is proposed to fill this gap as a software toolbox with the ability to analyze and synthesize PWA controllers for nonlinear systems. The toolbox proposed in this paper has been written to serve as an educational as well as a modeling, analysis and synthesis tool. PWATOOLS uses Yalmip, SeDuMi and PENBMI to find solutions for the sufficient conditions for stability analysis of the models or the synthesis of PWA controllers. An example in active flutter supression illustrates the use of this new toolbox.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.016 |
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".