Thesis: Adaptive Gearbox Control through machine learning
The purpose of the thesis work is to investigate machine learning methods for adaptive gearbox control.
Deviations in the control of a gear shift can cause discomfort and decrease the lifetime of the components. The objective is to identify these deviations using machine learning methods. This information can then be used to apply corrective control measures.
The students should preferably have a background in computer science, machine learning, optimization theory, mechatronics or hydraulics engineering.
Experience and interest in automotive applications is valuable. Own drive, curiosity, ability to analyze, conclude and document is important skills.
Description of thesis work
The study should contain a theoretical part where different machine learning methods are compared and implemented in a simulation study.
Finally the most suitable method can be implemented on production hardware and be compared with the existing implemented control in a transmission test bench or machine.
The focus of the thesis will be on investigation through data analysis and simulation, implementation and test on testbench or machine and documentation of algorithms.
Thesis Level: Master
Starting date: Jan-Feb 2020
Number of students: 2
Anders Löfgren, Function Leader, +46(0)-70 003 4671
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