Search Results

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.

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).

A closed-loop articulated vehicle-driver model, incorporating the path errors, lateral accelerations of the two units and the rate of steering, is proposed to study the directional control behavior of the driver. The closed-loop driver-vehicle model is formulated upon integrating the yaw-plane model of a five-axle articulated vehicle and a comprehensive driver model. The driver model, incorporating the delays associated with the limb movement and muscle activities, is developed with an objective to minimize the lateral acceleration of vehicle, and the lateral position and orientation errors between the previewed and the actual path of the tractor. Various parameters required to describe the driver's contributions are identified through minimizing a weighted performance index subject to an array of limit constraints established from the reported data.

Development of a micro-processor based early warning safety device that can detect and warn the drivers of impending dynamic instabilities is discussed to improve the operational safety of articulated freight vehicles. Directional dynamics of articulated freight vehicles are investigated to determine the key dynamic response parameters that can best describe the onset of rollover and jackknife instabilities. The feasibility of identified key response parameters is further investigated in view of various vehicle design and operating conditions, and ease of on-line acquisition and analyses. The study concludes that a general stability criteria can be established to identify impending roll and jackknife instabilities, and a safety monitor can be conceived to provide an early warning to the driver.