A non-linear dynamic model for agricultural vehicles constructed in digital space

In response to the shortage of agricultural labor due to an aging population, the concept of "smart agriculture" has emerged, which attaches great importance to the accurate modeling of real agricultural information in digital space, to realize higher-level intelligent management and control. As an important smart agricultural technology, accurate simulation of agricultural off-road vehicles in digital space can help enhance agricultural productivity, such as optimizing farming task schedule. To achieve this smart agriculture technology, it is necessary to construct high-precision agricultural vehicle models suitable for various agricultural environments in digital space. However, constructing highly precise, realistically performing dynamic models for agricultural vehicles in digital space remains a challenge. The performance of simple kinematic models and traditional linear dynamic models of agricultural vehicles is very limited: these models are only accurate under small side slip conditions, but not suitable for environments that would cause large side slip of agricultural vehicles, such as wet or soft soil. To solve this problem, we here propose a non-linear dynamic model for agricultural vehicles in digital space. First, we combine a simplified non-linear tire model and side slip angle estimation method to make a lateral force-estimation method. We then use the lateral force estimation and the Unity physics engine to construct a non-linear dynamic model for agricultural vehicles in digital space. The validation tests of both digital space and real-world experiments prove that: (1) The proposed model can accurately simulate the status of real tractors even with a simplified set of parameters. (2) The proposed non-linear model has a wider range of environmental applicability than that of traditional linear model, especially for those environments that may cause large side slip. (3) The proposed non-linear model has strong practicality and can cope with the changing agricultural environments by simply tuning the model parameters.