Pieter Schalk Els

Accidents caused by wheeled mining machines contribute to approximately 30% of injuries and fatalities in the global mining industry. Wheeled mining machines have limited driver assist features when compared to the passenger vehicle market and are typically limited to collision avoidance by braking. These products are often subject to false positive interventions leading to production losses, increased wear, and resistance to adopt the technology by end users. This study proposes a sampling-based method to expand the collision avoidance by braking approach to include steering. The sampling method is based on the vehicle’s kinematics and the application of a Gaussian distribution to the steering rate to determine the probability of a collision occurring. Initial results indicate that the inclusion of steering rate on the collision prediction model may increase the operator’s situational awareness, leading to fewer false positives.

This paper presents a non-linear integrated control strategy that primarily focuses maintaining vehicle lateral stability using active front steering and differential braking. The proposed control strategy utilises a non-linear model predictive controller to improve lateral stability. A stable linear reference model is used for reference generation. By including the understeer gradient in the reference model, different kinematic responses are obtained from the controlled vehicle. The prediction model utilises the road friction estimate to create dynamic stability constraints that include rollover and sliding of the vehicle. The design of the model predictive controller allows easy activation of different control actuators and dynamic modification to the control behaviour. The control methodology is validated using MATLAB/Simulink and a validated MSC ADAMS model. A sensitivity analysis is conducted to identify the susceptibility of the control strategy to various parameters and states.