Safe Learning Reference Governor: Theory and Application to Fuel Truck Rollover Avoidance
Not on the map. Why?
Abstract (arXiv)
This paper proposes a learning reference governor (LRG) approach to enforce state and control constraints in systems for which an accurate model is unavailable, and this approach enables the reference governor to gradually improve command tracking performance through learning while enforcing the constraints during learning and after learning is completed. The learning can be performed either on a black-box type model of the system or directly on the hardware. After introducing the LRG algorithm and outlining its theoretical properties, this paper investigates LRG application to fuel truck (tank truck) rollover avoidance. Through simulations based on a fuel truck model that accounts for liquid fuel sloshing effects, we show that the proposed LRG can effectively protect fuel trucks from rollover accidents under various operating conditions.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 41 references that are in the map. First posted on arXiv 22 Jan 2021.
Builds on
8 of 41 references.
References · 8
- 2017Safe Model-based Reinforcement Learning with Stability Guaranteescited by 366
- 2019End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Taskscited by 289
- 2018Learning-based Model Predictive Control for Safe Explorationcited by 120
- 2019Adaptive Safety with Control Barrier Functionscited by 25
- 2019Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systemscited by 19
- 2016Reference Governor Strategies for Vehicle Rollover Avoidancecited by 1
- 2019A Reference Governor for Nonlinear Systems with Disturbance Inputs Based on Logarithmic Norms and Quadratic Programmingcited by 1
- 2020Nonlinear Dynamic Systems Parameterization Using Interval-Based Global Optimization: Computing Lipschitz Constants and Beyondcited by 1
Built on by
Not cited yet.