<p>The vehicle industry has an endless appetite to get better. Often, this appetite is justified by the need of the hour. In the agricultural space, this translates to improving agricultural productivity in the face of population growth, reduced arable land and shortage of skilled farm labor. As for torsional vibrations, which have been around ever since the wheel was invented, the problem gets redefined with new regulations demanding new powertrains with improved fuel efficiency and reduced emissions.</p><p>A solution to the agriculture problem, involves efficiently automating the harvesting process.The first section of this thesis covers the ‘Auto-Unload’ where the goal of automation is achieved. This was done by building a simulation framework that was used to develop and synthesize the ‘AutoUnload’ controller. This controller was later deployed on a combine and a successful unloading on-the-go was demonstrated with a combine, tractor, and tractor-driven grain cart.</p><p>The solution to the second problem about drivetrain vibrations involved deriving a mathematical model for simulating the powertrain of a medium-duty truck. This was done to confirm resonance seen during testing done on a chassis dynamometer. The consequent control strategy to mitigate undesired vibration was to move the torque excitation away from the natural frequency of the system. This was achieved by a ‘gear-shifting’ algorithm. Comparison between on-road tests with and without the ‘gear-shifting’ algorithm showed that such a control strategy can effectively eliminate resonance. The solution methodology developed in this work is robust and transferable to higher engine torques and harvest speeds.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/23804310 |
| Date | 31 July 2023 |
| Creators | Shveta Dhamankar (16709415) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Model-Based_Approach_for_Resilient_Vehicle_Operation/23804310 |
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