On Uniqueness of Filippov’s Solutions for Non-Smooth Systems Having Multiple Discontinuity Surfaces With Applications to Control Engineering
Bibliographic record
Abstract
The analysis of the uniqueness of Filippov’s solutions to non-smooth control systems is important before the solutions can be sought. Such an analysis is extremely challenging when the discontinuity surface is the intersecting discontinuity surfaces. The key step is to study the intersections of the convex sets from Filippov’s inclusions and the sets containing vectors tangent to the discontinuity surfaces. Due to the fact that all the elements of these sets are functions of the states and time and their numerical values can not be obtained before the uniqueness of the solution is analyzed, the determination of such intersections, symbolically, is extremely difficult. In this paper, we propose to firstly transform the control system to a new state space where the discontinuity surfaces can be written in special forms. Secondly, we expand the sets associated with Filippov’s inclusion such that the determinations of the intersections become feasible. Two examples of practical non-smooth control systems are presented to demonstrate the efficacy of the 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".