# Robust Action Governor for Uncertain Piecewise Affine Systems with Non-convex Constraints and Safe Reinforcement Learning

arXiv: https://arxiv.org/abs/2207.08240 · HTML: https://mlsanity.com/paper/2207.08240/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 55 references that are in the map.
- First posted on arXiv 17 Jul 2022.

## Builds on (most cited first, 11 of 11)

- [Control Barrier Function Based Quadratic Programs for Safety Critical Systems](https://mlsanity.com/paper/1609.06408/) (2016)
- [A General Safety Framework for Learning-Based Control in Uncertain Robotic Systems](https://mlsanity.com/paper/1705.01292/) (2017)
- [Adaptive Safety with Control Barrier Functions](https://mlsanity.com/paper/1910.00555/) (2019)
- [Safe Multi-Agent Interaction through Robust Control Barrier Functions with Learned Uncertainties](https://mlsanity.com/paper/2004.05273/) (2020)
- [Warm Start of Mixed-Integer Programs for Model Predictive Control of Hybrid Systems](https://mlsanity.com/paper/1910.08251/) (2019)
- [Safe Reinforcement Learning Using Robust Action Governor](https://mlsanity.com/paper/2102.10643/) (2021)
- [Action Governor for Discrete-Time Linear Systems with Non-Convex Constraints](https://mlsanity.com/paper/2005.08358/) (2020)
- [Safety of Dynamical Systems with Multiple Non-Convex Unsafe Sets Using Control Barrier Functions](https://mlsanity.com/paper/2106.06330/) (2021)
- [Adaptive Robot Navigation with Collision Avoidance subject to 2nd-order Uncertain Dynamics](https://mlsanity.com/paper/2005.12599/) (2020)
- [Spacecraft Attitude Control with Nonconvex Constraints: An Explicit Reference Governor Approach](https://mlsanity.com/paper/1905.00387/) (2019)
- [Chance-Constrained Controller State and Reference Governor](https://mlsanity.com/paper/2010.01710/) (2020)

## Built on by (most cited first, 1 of 1)

- [Safe Control and Learning Using Generalized Action Governor](https://mlsanity.com/paper/2211.12628/) (2022)

## Abstract (arXiv)

The action governor is an add-on scheme to a nominal control loop that monitors and adjusts the control actions to enforce safety specifications expressed as pointwise-in-time state and control constraints. In this paper, we introduce the Robust Action Governor (RAG) for systems the dynamics of which can be represented using discrete-time Piecewise Affine (PWA) models with both parametric and additive uncertainties and subject to non-convex constraints. We develop the theoretical properties and computational approaches for the RAG. After that, we introduce the use of the RAG for realizing safe Reinforcement Learning (RL), i.e., ensuring all-time constraint satisfaction during online RL exploration-and-exploitation process. This development enables safe real-time evolution of the control policy and adaptation to changes in the operating environment and system parameters (due to aging, damage, etc.). We illustrate the effectiveness of the RAG in constraint enforcement and safe RL using the RAG by considering their applications to a soft-landing problem of a mass-spring-damper system.