Scalable Safety-Preserving Robust Control Synthesis for Continuous-Time\n Linear Systems
Bibliographic record
Abstract
We present a scalable set-valued safety-preserving controller for constrained\ncontinuous-time linear time-invariant (LTI) systems subject to additive,\nunknown but bounded disturbance or uncertainty. The approach relies upon a\nconservative approximation of the discriminating kernel using robust maximal\nreachable sets---an extension of our earlier work on computation of the\nviability kernel for high-dimensional systems. Based on ellipsoidal techniques\nfor reachability, a piecewise ellipsoidal algorithm with polynomial complexity\nis described that under-approximates the discriminating kernel under LTI\ndynamics. This precomputed piecewise ellipsoidal set is then used online to\nsynthesize a permissive state-feedback safety-preserving controller. The\ncontroller is modeled as a hybrid automaton and can be formulated such that\nunder certain conditions the resulting control signal is continuous across its\ntransitions. We show the performance of the controller on a twelve-dimensional\nflight envelope protection problem for a quadrotor with actuation saturation\nand unknown wind disturbances.\n
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".