Articulated Vehicle Lateral Stability Management via Active Rear-Wheel Steering of Tractor Using Fuzzy Logic and Model Predictive Control

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Abstract
In-phase rear-wheel steering, where rear wheels are steered in the same direction of front wheels, has been widely investigated in the literature for vehicle stability improvements along with stability control systems. Much faster response can be achieved by steering the rear wheels automatically during an obstacle avoidance maneuver without applying the brakes where safe stopping distance is not available. Sudden lane change movements still remain challenging for heavy articulated vehicles, such as tractor and semitrailer combinations, particularly on roads with low coefficient of adhesion. Different lateral accelerations acting on tractor and semi-trailer may cause loss of stability resulting in jackknifing, trailer-swing, rollover, or slip-off. Several attempts have been made in the literature to use active steering of semi-trailer’s rear wheels to prevent jackknifing and rollover. However, loss of stability in an articulated vehicle is usually caused by an oversteered tractor, and the semitrailer’s rear wheels have little effect on the tractor’s directional control. In this study, viability of active rear-wheel steering of tractor to maintain the stability of an articulated vehicle during a high-speed obstacle avoidance maneuver is investigated. Two different controllers, fuzzy logic and linear model-based predictive controllers, are proposed to minimize the off-tracking behavior of an articulated vehicle. The controllers were tested in IPG/TruckMaker environment with MATLAB/Simulink interface on roads with various coefficient of adhesions, performing single lane change maneuvers. The simulated results showed that jackknifing occurring right after sudden lane changes can be successfully prevented using the tractor’s active rear-wheel steering based on model predictive control algorithm when the feedback gains are tuned correctly.

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