  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 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.
