The farming industry worldwide is facing challenges in order to be more productivity, efficient and leave a smaller footprint on the environment. The success relies on the efficient operation of the agricultural machinery including the agricultural tractor. One of the major and even one of the most energy consuming operations for an agricultural tractor is soil preparation, especially ploughing. To be efficient, the tractor should be able to transform the highest possible amount of the engine power into a pulling force on the implements, i.e. the plough. From theory of tire mechanics, it is known, that slip is necessary in order to transmit force. Slip, which is relative speed between the pulling wheel and the ground, is also an expression of loss. Hence, it is crucial to have just enough slip to pull the implement without wasting to much energy.
The well-known electro-hydraulic hitch control system for agricultural tractors facilitates a control scheme, that based on sensors can relax the hitch by lifting the implement, reducing the pulling load and limits the slip without compromising working depth too much. Even though this technology is mature, the tuning of the control parameters poses a major challenge, partly because the set of parameters must fit both very hard dry soils as well as wet soil – and these conditions are difficult to obtain within a short time period. In other words, identification and tuning of the parameters can be challenging in terms of time, conditions and repeatability resulting in semi-optimum set of parameters.
In this paper a model-in-the-loop tuning approach is proposed and demonstrated. By harvesting experimental data, this can be used as foundation for standardized test field in order to conduct repeatable tests. These experimental data can be utilized in a simulation model, that represents the mechanical properties for the plough, tractor and tire-soil interaction. The output of the simulation serves as signal to stimulate the controller on the tractor. By this approach the hitch control parameters can be tuned and improved any time a year without available fields. With the possibility to conduct repeatable tests with no variation on the boundary conditions, the loop time for each iteration can as well be reduced significantly.