Online Controller Setpoint Optimization for Traction Control Systems Applied to Construction Machinery

Abstract

The development of a suitable traction control system for off-road heavy machinery is complicated by several different factors, which differentiate these machines from typical on-road systems. One such difficulty arises from the fact that they are often operated on ground conditions which can vary widely and rapidly. Due to this, traction control systems designed for these vehicles must be robust to a large array of surface types, and they must be capable of reacting quickly to significant changes in those types. In order to accomplish this, this paper proposes an online parameter optimization technique suitable for tuning the setpoint of a control system to maximize the tractive potential of a construction vehicle in real time. The traction control principle itself is based on selectively braking wheels which are slipping. It also attempts to account for the interactions of the transmission systems that deliver power from the engine to the wheels. This research uses a wheel loader as a reference machine for assessing controller performance. Drawing on previous work in simulation and controller design, a system model was developed which incorporates the vehicle dynamics of the machine as well as the behavior of the electrohydraulic brakes. This system model was leveraged to understand the effect of different optimization schemes on the performance of the traction control. The self-tuning algorithm is based on a compound optimization method utilizing both a system identification component and a parameter tuning component. The first part optimizes the model parameters to fit it as well as possible to measured slip-friction data. Based on the results of this, the second part draws from theories of wheel traction to maximize a balance of pushing force and traction effectiveness. The result is a method which can achieve the proper setpoint based on real-time data describing the ground condition. This system was run first in simulation and then on a modified vehicle system. In both cases, the algorithm allows the controller to find better setpoints to improve the traction control performance online.

Traction Control Development for Heavy-Duty Off-Road Vehicles Using Sliding Mode Control

“…Due to the critical nature of the tire-ground surface interaction, it is important that TC systems for off-road machines are robust to significant changes in operating condition, such as driving from a concrete surface onto slick mud or ice (as shown in Schreiber [14]). Therefore, past work for these machines has focused on generating a control structure which can optimize its own parameters in order to find the best setup for a given operating condition [15][16][17]. Instead, by taking advantage of the design of sliding mode control, this work generates a controller which is naturally robust to changes in operating condition.…”

“…By solving a static force balance treating the implement as a loaded structure, the resistive force generated by the barrier can be calculated. More information on this methodology can be found in [17]. Using these measurements, the performance of the controller was assessed.…”

A method of online traction parameter identification and mapping

“…For instance, Kim and Lee (2018) used it in a slip controller. Alexander et al (2018) proposed using a data buffer coupled with an parameter optimization algorithm for traction parameter identification. Pentos and Pieczarka (2017) used an artificial neural network to predict the influence of the soil texture, soil moisture and compaction etc.…”

Model Development for Off-Road Traction Control: A Linear Parameter-Varying Approach

“…Another solution is adaping the various on-road tire models for off-road applications, such as the Pacejka tire model [17]. In [18], the slip-friction relationship was described using the Pacejka model fitted to the measurement data. Then, it was combined with the traction effectiveness to determine an optimal slip value for the traction controller.…”