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Safe Control and Learning Using Generalized Action Governor

arXiv 2211.12628 ↗First posted 22 Nov 2022eess.SY cs.AI cs.SYcited by 0

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Abstract (arXiv)

This paper introduces the Generalized Action Governor (AG), a supervisory scheme that augments a nominal closed-loop system with the capability to enforce state and input constraints through online action adjustment. We develop a generalized AG theory for discrete-time systems under bounded uncertainties, and relax the usual requirement of positive invariance to returnability of a safe set. Based on the theory, we present tailored AG design procedures for linear systems and for discrete systems with finite state and action spaces. We further study safe online learning enabled by the AG and present two safe learning strategies, namely safe Q-learning and safe data-driven Koopman operator-based control, both integrated with the AG to guarantee constraint satisfaction during learning. Numerical results illustrate the proposed methods.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 39 references that are in the map. First posted on arXiv 22 Nov 2022.

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15 of 39 references.

References · 15

  1. 2016Control Barrier Function Based Quadratic Programs for Safety Critical Systemscited by 261
  2. 2019End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Taskscited by 256
  3. 2016Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive controlcited by 134
  4. 2018A predictive safety filter for learning-based control of constrained nonlinear dynamical systemscited by 91
  5. 2019Safe Reinforcement Learning Using Robust MPCcited by 72
  6. 2020Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functionscited by 61
  7. 2019Robust Model Predictive Shielding for Safe Reinforcement Learning with Stochastic Dynamicscited by 45
  8. 2020Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Functioncited by 42
  9. 2020Safe Reinforcement Learning via Projection on a Safe Set: How to Achieve Optimality?cited by 29
  10. 2020Derivative-Based Koopman Operators for Real-Time Control of Robotic Systemscited by 12
  11. 2021Safe Reinforcement Learning Using Robust Action Governorcited by 12
  12. 2021Scalable Learning of Safety Guarantees for Autonomous Systems using Hamilton-Jacobi Reachabilitycited by 10
3 more
  1. 2020Action Governor for Discrete-Time Linear Systems with Non-Convex Constraintscited by 3
  2. 2019Data-driven identification of vehicle dynamics using Koopman operatorcited by 2
  3. 2022Robust Action Governor for Uncertain Piecewise Affine Systems with Non-convex Constraints and Safe Reinforcement Learningcited by 1

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