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

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.

STUDY OF THE BEHAVIOR OF TANK TRUCK WITH LIQUID CARGO IN CURVED MOVEMENT

The principles of calculating the stability margin and overturning moment to ensure the safe-ty of road traffic of a heavy-duty vehicle on curved road sections are considered. Two stages of the behavior of a fire-fighting tanker are considered: during slow sliding in a skid and the impact of the car's wheels on an obstacle, followed by overturning.

Model-Free Adaptive Control for Tank Truck Rollover Stabilization

Influenced by lateral liquid sloshing in partially filled tanks, tank vehicles are apt to encounter with rollover accidents. Due to its strong nonlinearity and loading state uncertainty, it has great challenges in tank vehicle active control. Based on the model-free adaptive control (MFAC) theory, the roll stability control problem of tank trucks with different tank shapes and liquid fill percentages is explored. First, tank trucks equipped with cylinder or elliptical cylinder tanks are modelled, and vehicle dynamics is analyzed. This dynamic model is used to provide I/O data in the controlled system. Next, the control objective of tank vehicle rollover stabilization is analyzed and the controlled variable is selected. Subsequently, differential braking and active front steering controller are designed by MFAC algorithm. Finally, the effectiveness of the designed controllers is verified by simulation, and difference between the controllers is analyzed. The controller designed by MFAC algorithm is proven to be adaptive to vehicle loading and driving states. The controlled system has great robustness.

Controls at the starting point for preventing accidents in the road transport of dangerous products / Controles no ponto de partida para prevenção de acidentes no transporte rodoviário de produtos perigosos

This article discusses activities related to the prevention of road accidents with dangerous products that precede trips by tank truck drivers. The Brazilian normative framework (laws, decrees, and norms), the Haddon Matrix as a systemic prevention model, and the premises of the Olho Vivo nas Estradas Program of the Brazilian Chemical Industry Association constitute the conceptual bases of the research. The empirical approach consists of semi-structured interviews with drivers of a fuel distributor who travel along the BR-116. The results indicate that the company has training practices and disclosure of recent accidents. The workgroup dominates the normative knowledge and preventive procedures, being the main concern of the interviewees with the carelessness practiced by drivers who travel the roads in all types of vehicles (trucks, automobiles, motorcycles).