Master Thesis Machine Learning and Motion Coordination
Göteborg, SE, 417 15
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Master Thesis Proposal - Machine Learning and Motion Coordination of Battery Electric Heavy Vehicles
For the effective motion control of heavy vehicles, a motion coordination functionality is fundamental since it ensures that vehicles can perform desired maneuvers safely and efficiently by distributing control efforts among multiple actuators. One of the challenges lies in determining how to allocate control efforts among these actuators to achieve the desired motion while considering constraints like actuator limits. In a battery electric vehicle (BEV), it could involve coordinating the actions of the steering system, wheel braking, and electric motors to maintain stability and control during a maneuver. Effective control allocation ensures that the vehicle can respond accurately to driver inputs while, for example, minimizing power losses.
The control allocation problem could be formulated as an optimization problem. The objective is then to minimize a cost function that represents the difference between the desired and the optimized vehicle response, subject to constraints such as actuator limits and vehicle dynamics. Commonly mathematical models are used to represent the behavior of the vehicle and its actuators, which could be difficult to identify, since they can vary with operating conditions, such as load and speed.
Machine Learning (ML) techniques can be employed to identify and update the parameters of the mathematical model used in control allocation. These techniques can learn from data collected during vehicle operation to improve the accuracy of the model over time.
The objectives of the master's thesis are as follows:
1. Investigate previous work done in the area of motion coordination.
2. Investigate the effect of errors in model parameters of the control allocation problem.
3. Develop a ML functionality to estimate model parameters.
4. Test the performance of the developed functionalities using simulations with different scenarios.
The thesis work will require machine learning, control theory, and vehicle dynamics skills. Interest in reinforcement learning is seen as a benefit. MATLAB will be used as the primary development environment. The work will be carried out at Volvo Group Trucks Technology. The thesis is recommended for one or two students with vehicle dynamics and/or control analysis profile with good mathematical skills. Thesis start: Jan 2025.
If you find this proposal interesting, send your application with CV and grades through the Volvo Group-Careers website.
Supervisors:
Esteban Gelso – Volvo GTT
Maliheh Sadeghi Kati – Volvo GTT
Umur Erdinc – Volvo GTT
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