Construction machines represent a particular set of difficulties when modelling their system dynamics. Due to their generally low velocities and unorthodox operating conditions, the standard modelling equations used to simulate the behaviour of highway vehicles can have a poor behaviour for these systems. This paper sets forth a vehicle model which is suitable for construction machines, which travel at low velocities and encounter significant external forces in daily operation. It then shows the work done in validating the machine model with experimental data. First, the overall vehicle dynamics are developed, including a model for the machine behaviour when pushing against a resistive force. Then, a wheel force generation model suitable for low-velocity systems is discussed. Finally, pertinent experimental results are presented. Two different model validation tests were run. Both tests generated results which were matched well by the simulation model. In fact, the model matches experimental data reasonably well for both roading and pushing conditions. This indicates that the modelling methods described in this work are appropriate for the modelling of low-velocity systems such as wheel loaders and other construction machinery.
Many modern off-road construction machines incorporate traction control systems to provide better performance and stability in harsh driving conditions. These systems are capable of controlling wheel slip in such a way that the tractive force is increased, tire consumption is reduced, and the overall safety of the machine is improved. However, the driving surface conditions can have a strong impact on the optimal control parameters for the traction control system. This paper sets forth a method of automatically tuning the controller parameters in real time, so that the system can maximize the tractive force on its own.Toward this end, a simple longitudinal wheel dynamics model is developed using a construction machine as a reference. This model incorporates considerations for the generation of tire force, wheel slip dynamics and machine transmission. Then, a simple traction control structure using proportional-integral-derivative (PID) control is presented which attempts to keep the machine wheels from slipping excessively. Finally, a real-time optimization scheme using the extremumseeking algorithm was included in the system in order to automatically improve the setpoint of the controller by maximizing the pushing force of the machine. Using the vehicle model of the system, the auto-tuning controller is tested to determine the capability of the system to improve the performance. The optimization scheme allows the controller to find the optimal point, meaning that the output force can be increased when starting at a poor setpoint. Given the availability of a proper feedback signal, this system should be widely applicable to a wide range of different vehicle systems for incorporating traction control.
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.
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