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In this paper, a gain-scheduling optimal control approach is proposed to enhance yaw stability of articulated commercial vehicles through active braking of the proper wheel(s). For this purpose, an optimal feedback control is used to design a family of yaw moment controllers considering a broad range of vehicle velocities. The yaw moment controller is designed such that the instantaneous tractor yaw rate and articulation angle responses are forced to track the target values at each specific vehicle velocity. A gain scheduling mechanism is subsequently constructed via interpolations among the controllers. Furthermore, yaw moments derived from the proposed controller are realized by braking torque distribution among the appropriate wheels. The effectiveness of the proposed yaw stability control scheme is evaluated through software-in-the-loop (SIL) co-simulations involving Matlab/Simulink and TruckSim under lane change maneuvers.

A range of axle suspensions, comprising hydro-pneumatic struts and diverse linkage configurations, have evolved in recent years for large size mining trucks to achieve improved ride and higher operating speeds. This paper presents a comprehensive analysis of different independent front suspension linkages that have been implemented in various off-road vehicles, including a composite linkage (CL), a candle (CA), a trailing arm (TA), and a double Wishbone (DW) suspension applied to a 190 tons mining truck. Four different suspension linkages are modeled in MapleSim platform to evaluate their kinematic properties. The relative kinematic properties of the suspensions are evaluated in terms of variations in the kingpin inclination, caster, camber, toe-in and horizontal wheel center displacements considering the motion of a hydro-pneumatic strut. The results revealed the CL and DW suspensions yield superior kinematic response characteristics compared to the CA and TA suspensions.

An Active Independent Front Steering (AIFS) offers attractive potential for realizing improved directional control performance compared to the conventional Active Front Steering (AFS) system, particularly under more severe steering maneuvers. The AIFS control strategy adjusts the wheel steer angles in an independent manner so as to utilize the maximum available adhesion at each wheel/road contact and thereby compensate for cornering loss caused by the lateral load transfer. In this study, the performance potentials of AIFS are explored for vehicles experiencing greater lateral load transfers during steering maneuvers such as partly-filled tank trucks. A nonlinear yaw plane model of a two-axle truck with limited roll degree-of-freedom is developed to study the performance potentials of AIFS under different cargo fill conditions.

Compared to the vehicles with conventional steering, the articulated frame steer vehicles (ASV) are known to exhibit lower directional and roll stability limits. Furthermore, the tire interactions with relatively rough terrains could adversely affect the directional and roll stability limits of an ASV due to terrain-induced variations in the vertical and lateral tire forces. It may thus be desirable to assess the dynamic safety of ASVs in terms of their directional control and stability limits while operating on different terrains. The effects of terrain roughness on the directional stability limits of an ASV are investigated through simulations of a comprehensive three-dimensional model of the vehicle with and without a rear axle suspension. The model incorporates a torsio-elastic rear axle suspension, a kineto-dynamic model of the frame steering struts and equivalent random profiles of different undeformable terrains together with coherence between the two tracks profiles.

A two-stage preview strategy is proposed to characterize steering control properties of commercial vehicle drivers. The strategy includes a near and a far preview points to describe the driver control of lateral path deviation and vehicle orientation. A human driver model comprising path error compensation and dynamic motions of the limb is subsequently formulated and integrated to a yaw-plane model of an articulated vehicle. The coupled driver-vehicle model is analyzed under an evasive steering maneuver to identify limiting values of the driver control parameters through minimization of a generalized performance index comprising driver's steering effort, path deviations and selected vehicle states. The performance index is further analyzed to identify relative contributions of different sensory feedbacks, which may provide important guidance for designs of driver-assist systems (DAS).