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This paper proposes a learning reference governor (LRG) approach to enforcestate and control constraints in systems for which an accurate model isunavailable, and this approach enables the reference governor to graduallyimprove command tracking performance through learning while enforcing theconstraints during learning and after learning is completed. The learning canbe performed either on a black-box type model of the system or directly on thehardware. After introducing the LRG algorithm and outlining its theoreticalproperties, this paper investigates LRG application to fuel truck (tank truck)rollover avoidance. Through simulations based on a fuel truck model thataccounts for liquid fuel sloshing effects, we show that the proposed LRG caneffectively protect fuel trucks from rollover accidents under various operatingconditions.

Safe Learning Reference Governor: Theory and Application to Fuel Truck Rollover Avoidance