The dissertation addresses challenges in the development of autonomous systems for agricultural applications, using a novel tractor as its experimental platform. Our contributions span three core areas: model-based parameter identification, the realization of a Digital Twin, and the design of an optimal path-tracking controller. Compared with the attention given to conventional road vehicle dynamics, off-road agricultural vehicle dynamics remains a relatively unexplored topic. A central challenge in this field lies in the identification of the parameters that govern the contact interaction between wheels and soil, as it critically influences the vehicle’s behavior. This means that, when developing a mathematical model describing the tractor dynamics, precise knowledge of these parameters is required in order to obtain a model capable of accurately predicting the system’s behavior. Traditional approaches to parameter identification typically rely on extensive sensor instrumentation and controlled, lab-based experiments, rendering them impractical for daily field operations. In this work, we propose an approach that combines standard operational data, obtained from satellite and on-board systems, with mathematical models of the vehicle behavior, for the purpose of identifying system parameters. This allows for model-based identification without the need for additional measurement equipment. The resulting parametric models are then evaluated using performance metrics assessing their predictive accuracy. Based on these models, we propose a Digital Twin architecture leveraging state-of-the-art simulation tools to provide a digital representation of the system, enabling virtual testing, and a consequent seamless transition from simulation to deployment in development processes. The utility of a Digital Twin is demonstrated through the development of a Model Predictive Control (MPC) strategy for accurate path tracking. High positional accuracy in path tracking is indeed required in advanced agricultural approaches, such as the so-called precision agriculture framework, in order to actually achieve the targeted efÏciency gains. The proposed controller, which is based on a simplified model of the kinematic behavior of the tractor, is first validated in simulation, and then successfully deployed in field experiments, showcasing the Digital Twin as a key enabler for bridging the gap between control design and real-world implementation.
Ruggero Simonelli (Mon,) studied this question.