Safe Control and Learning Using the Generalized Action Governor

2022-11-22 · Nan Li, Yutong Li, Ilya Kolmanovsky, Anouck Girard, H. Eric Tseng, Dimitar Filev

This article introduces a general framework for safe control and learning based on the generalized action governor (AG). The AG is a supervisory scheme for augmenting a nominal closed-loop system with the ability of strictly handling prescribed safety constraints. In the first part of this article, we present a generalized AG methodology and analyze its key properties in a general setting. Then, we introduce tailored AG design approaches derived from the generalized methodology for linear and discrete systems. Afterward, we discuss the application of the generalized AG to facilitate safe online learning, which aims at safely evolving control parameters using real-time data to enhance control performance in uncertain systems. We present two safe learning algorithms based on, respectively, reinforcement learning and data-driven Koopman operator-based control integrated with the generalized AG to exemplify this application. Finally, we illustrate the developments with a numerical example.

📄 PDF Abstract BibTeX arXiv:2211.12628

Code (0)

등록된 구현이 없습니다.

Tasks

reinforcement-learning Reinforcement Learning (RL)

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

2022-07-17 · Yutong Li, Nan Li, H. Eric Tseng, Anouck Girard 외

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…

RAG Reinforcement Learning (RL)Safe Reinforcement Learning

Safe Reinforcement Learning Using Robust Action Governor

2021-02-21 · Yutong Li, Nan Li, H. Eric Tseng, Anouck Girard 외

Reinforcement Learning (RL) is essentially a trial-and-error learning procedure which may cause unsafe behavior during the exploration-and-exploitation process. This hinders the application of RL to real-world control pr…

RAG reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1

Control Barrier Function for Linearizable Systems with High Relative Degrees from Signal Temporal Logics: A Reference Governor Approach

2023-09-15 · Kaier Liang, Mingyu Cai, Cristian-Ioan Vasile

This paper considers the safety-critical navigation problem with Signal Temporal Logic (STL) tasks. We developed an explicit reference governor-guided control barrier function (ERG-guided CBF) method that enables the app…

Optimization-Free Constrained Control with Guaranteed Recursive Feasibility: A CBF-Based Reference Governor Approach

2026-04-05 · Satoshi Nakano, Emanuele Garone, Gennaro Notomista arxiv

This letter presents a constrained control framework that integrates Explicit Reference Governors (ERG) with Control Barrier Functions (CBF) to ensure recursive feasibility without online optimization. We formulate the r…

Reference Governor Design in the Presence of Uncertain Polynomial Constraints

2022-11-11 · Rick Schieni, Chengwei Zhao, Michael Malisoff, Laurent Burlion

Reference governors are add-on schemes that are used to modify trajectories to prevent controlled dynamical systems from violating constraints and so are playing an increasingly important role in aerospace, robotic, and …