Automatic traction control for articulated off-road vehicles

TL;DR

This paper compares three sensor-based algorithms for automatically controlling differential locks in off-road construction vehicles to improve traction and maneuverability, validated through simulation against a realistic vehicle model.

Contribution

It introduces and evaluates three novel automatic differential lock control algorithms based on different sensor inputs for articulated off-road vehicles.

Findings

Algorithms effectively improve traction control.

Sensor choice impacts algorithm sensitivity.

Validated against realistic vehicle simulations.

Abstract

Construction equipment is designed to maintain good traction, even when operating in difficult off-road conditions. To curb wheel slip, the vehicles are equipped with differential locks. A driver may engage/disengage the locks to switch between two distinct operating modes: the closed mode is characterized by greater off-road passability while the open mode allows better manoeuvrability. However, many drivers lack the education and experience required to correctly judge the terrain ahead of the vehicle and therefore engage/disengage the locks in a suboptimal fashion. An automatic traction control solution for locking and opening the differentials is hence desirable. This paper compares three on/off differential lock control algorithms, all derived from the same kinematic vehicle model but each relying on the availability of output signals from different sensors. The validity of the kinematic model and the algorithms' sensitivity to the values assumed by a couple of unobservable states, the wheel slip angles, is investigated by comparison to a realistic articulated hauler model in the multibody physics simulator MSC ADAMS.