mlsanityData to 7 Oct 2026

Linear model predictive safety certification for learning-based control

arXiv 1803.08552 ↗First posted 22 Mar 2018eess.SY cs.LG cs.SYcited by 34

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Abstract (arXiv)

While it has been repeatedly shown that learning-based controllers can provide superior performance, they often lack of safety guarantees. This paper aims at addressing this problem by introducing a model predictive safety certification (MPSC) scheme for polytopic linear systems with additive disturbances. The scheme verifies safety of a proposed learning-based input and modifies it as little as necessary in order to keep the system within a given set of constraints. Safety is thereby related to the existence of a model predictive controller (MPC) providing a feasible trajectory towards a safe target set. A robust MPC formulation accounts for the fact that the model is generally uncertain in the context of learning, which allows proving constraint satisfaction at all times under the proposed MPSC strategy. The MPSC scheme can be used in order to expand any potentially conservative set of safe states for learning and we prove an iterative technique for enlarging the safe set. Finally, a practical data-based design procedure for MPSC is proposed using scenario optimization.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Mar 2018.

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Citing papers per year: 2021: 8; 2022: 4; 2023: 9; 2024: 4; 2025: 4; 2026: 5.

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  1. 2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learningcited by 337
  2. 2023The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systemscited by 77
  3. 2021safe-control-gym: a Unified Benchmark Suite for Safe Learning-based Control and Reinforcement Learning in Roboticscited by 42
  4. 2021Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challengescited by 40
  5. 2023Physics-Informed Machine Learning for Modeling and Control of Dynamical Systemscited by 28
  6. 2022ISAACS: Iterative Soft Adversarial Actor-Critic for Safetycited by 24
  7. 2022Learning Control Policies for Stochastic Systems with Reach-avoid Guaranteescited by 21
  8. 2021Safe Reinforcement Learning with Chance-constrained Model Predictive Controlcited by 18
  9. 2024Gameplay Filters: Robust Zero-Shot Safety through Adversarial Imaginationcited by 12
  10. 2025A Survey of Safe Reinforcement Learning and Constrained MDPs: A Technical Survey on Single-Agent and Multi-Agent Safetycited by 12
  11. 2021Safe Adaptive Learning-based Control for Constrained Linear Quadratic Regulators with Regret Guaranteescited by 10
  12. 2022End-to-End Imitation Learning with Safety Guarantees using Control Barrier Functionscited by 10
22 more
  1. 2023Deception Game: Closing the Safety-Learning Loop in Interactive Robot Autonomycited by 9
  2. 2024Verified Safe Reinforcement Learning for Neural Network Dynamic Modelscited by 7
  3. 2024Towards safe and tractable Gaussian process-based MPC: Efficient sampling within a sequential quadratic programming frameworkcited by 6
  4. 2021Provably Correct Training of Neural Network Controllers Using Reachability Analysiscited by 5
  5. 2021Safe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action Spacescited by 5
  6. 2023Online Nonstochastic Control with Adversarial and Static Constraintscited by 5
  7. 2023Non-asymptotic System Identification for Linear Systems with Nonlinear Policiescited by 5
  8. 2024Reinforcement Learning with Ensemble Model Predictive Safety Certificationcited by 4
  9. 2023Multi-Step Model Predictive Safety Filters: Reducing Chattering by Increasing the Prediction Horizoncited by 3
  10. 2023Safe multi-agent motion planning under uncertainty for drones using filtered reinforcement learningcited by 3
  11. 2023Learning Predictive Safety Filter via Decomposition of Robust Invariant Setcited by 3
  12. 2022Fail-Safe Adversarial Generative Imitation Learningcited by 2
  13. 2025A Simple Approach to Constraint-Aware Imitation Learning with Application to Autonomous Racingcited by 2
  14. 2025Provably Optimal Reinforcement Learning under Safety Filteringcited by 2
  15. 2026Safe Reinforcement Learning via Recovery-based Shielding with Gaussian Process Dynamics Modelscited by 1
  16. 2026Uncertainty-Aware Predictive Safety Filters for Probabilistic Neural Network Dynamicscited by 1
  17. 2026Permissive Safety Through Trusted Inference: Verifiable Belief-Space Neural Safety Filters for Assured Interactive Roboticscited by 1
  18. 2021Learning Safety Filters for Unknown Discrete-Time Linear Systemscited by 0
  19. 2023Safe Online Dynamics Learning with Initially Unknown Models and Infeasible Safety Certificatescited by 0
  20. 2025Safely Learning Controlled Stochastic Dynamicscited by 0
  21. 2026All Models are Wrong, Knowing Where is Useful: On Model Uncertainty in Reinforcement Learningcited by 0
  22. 2026Safe Online Learning via Smooth Safety-Structured Policy Compositioncited by 0