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Certified-Predecessor Scenario Ellipsoidal Model Predictive Safety Filter

  • Task ID: math.mpsc_safety_filter
  • Domain: math
  • Subdomain: control_safety_mpc
  • Status: final
  • Benchmark set: seed42 (60 tasks)
  • Tags: model_predictive_control, safety_filter, learning_based_control, scenario_optimization, robust_invariance, data_driven_terminal_set, predecessor_closure, constrained_optimization, dynamical_systems

Runtime and requirements

  • Estimated time: 15-60 minutes
  • Python: >=3.11
  • Packages: numpy>=1.26, scipy>=1.11, cvxpy>=1.4, clarabel>=0.7, scs>=3.2
  • GPU required: no
  • Network required: no

Public input and output contract

Inputs

  • system.json (data): Schema-v6 constrained linear system, public disturbance-polytope scenarios, stabilizing feedback, scenario-design bounds, unlabelled terminal predecessor candidates, horizon, and objective parameters; no ellipsoid, accepted-candidate labels, or terminal facets.
  • public_cases.json (data): JSON object with an unlabelled cases list; each case contains state x and proposed action u_learning.

Outputs

  • analysis.py (code): Self-contained vector-valued data-driven linear safety filter.
  • certificate.json (data): Candidate ellipsoidal error certificate, support values, tightened polytopes, and the data-enlarged nominal terminal hull.

Public repository files

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Provenance

This task is included in the current seed42 benchmark set at dataset revision f11a199f71fb4b854ac43a1bf548d5df519141a9.

This page is generated only from files tracked in the public ASI-Bench repository at commit c9c1ba960854. Submission bundles, run logs, private scoring configuration, and private reference answers are not read by this page generator.