Modeling and parameter identification for agricultural machines

Abstract

A series of technical and social aspects have led the pressure to increase energy efficiency and reduce the level of environmental impact in agricultural production. Crop producers and equipment manufacturers have identified computerized assistance in operation management as an indispensable tool to optimize agricultural processes. A central place is occupied in this regard by the so called digital twins, computer models of a specific instance of an object or system, which are also capable of exchanging data with the physical counterpart and updating their state accordingly. The core of a digital twin is given by the digital mock‐up, or offline digital twin, that is the computer model of the system's physics. The goal of this work is to present the development of an offline digital twin for a novel gantry‐tractor type autonomous agricultural machine. The peculiarity of this system resides in its until now scarcely adopted mechanical design, and its ability to perform a range of processes by attaching different modules to its frame. After a brief introduction into the characteristics of a gantry‐type tractor and its role in the context of precision autonomous agriculture and controlled traffic farming, different components of the machine and dynamic phenomena are separately investigated and mathematically modeled. Particular focus is placed thereby on the study of the vehicle's longitudinal dynamics. Based on the software implementation of the mathematical models, the unknown parameters of the system model are estimated by means of nonlinear optimization techniques, using sensor data collected during field operation as the measurement reference. The accuracy of different estimation methods is then evaluated against experimental samples collected by sensors.