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Bayesian Optimization for Racing Aerial Vehicle MPC Tuning
In recent years, model predictive control, one of the most popular methods for controlling constrained systems, has benefitted from the advancements of learning methods. Many applications showed the potential of the cross fertilization between the two fields, i.e., autonomous drone racing, autonomous car racing, etc. Most of the research efforts have been dedicated to learn and improve the model dynamics, however, the controller tuning, which has a crucial importance, have not been studied much.
In recent years, model predictive control, one of the most popular methods for controlling constrained systems, has
benefitted from the advancements of learning methods. Many applications showed the potential of the cross fertilization between the two fields, i.e., autonomous drone racing, autonomous car racing, etc. Most of the research efforts have been dedicated to learn and improve the model dynamics, however, the controller tuning, which has a crucial importance, have not been studied much.
In recent years, model predictive control, one of the most popular methods for controlling constrained systems, has benefitted from the advancements of learning methods. Many applications showed the potential of the cross fertilization between the two fields, i.e., autonomous drone racing, autonomous car racing, etc. Most of the research efforts have been dedicated to learn and improve the model dynamics, however, the controller tuning, which has a crucial importance, have not been studied much.
The objective of this project is to implement an auto-tuning learning-based algorithm for Model Predictive Contouring Control in a racing drone setting. The controller will learn how to tune the controller weights by using specialized Bayesian optimization algorithms [1] that can explore the large dimensional controller parameters space. The learning algorithm will be first tested in simulation and then validated with hardware experiments on a racing aerial vehicle.
Your project would include:
- A literature research on the current state of the art about Bayesian optimization [1] for controller tuning and
on MPCC literature [2]
- Implementation of the state-of-the-art algorithms identified in the previous point
- Development of a tailored automatic controller parameters adaptation based on Bayesian optimization
- Simulation of the developed algorithms on a racing drone
- Test of the algorithms on a real racing drone
The thesis will be in collaboration between UZH Robotics and Perception group and ETH IDSC Intelligent Control Systems group.
[1] Fröhlich, Lukas P., Melanie N. Zeilinger, and Edgar D. Klenske. "Cautious Bayesian optimization for efficient and scalable policy search." Learning for Dynamics and Control. PMLR, 2021.
[2] A. Romero, S. Sun, P. Foehn, and D. Scaramuzza, “Model predictive contouring control for time-optimal quadrotor flight,” IEEE Trans. Robot., doi: 10.1109/TRO.2022.3173711.
The objective of this project is to implement an auto-tuning learning-based algorithm for Model Predictive Contouring Control in a racing drone setting. The controller will learn how to tune the controller weights by using specialized Bayesian optimization algorithms [1] that can explore the large dimensional controller parameters space. The learning algorithm will be first tested in simulation and then validated with hardware experiments on a racing aerial vehicle.
Your project would include:
- A literature research on the current state of the art about Bayesian optimization [1] for controller tuning and on MPCC literature [2]
- Implementation of the state-of-the-art algorithms identified in the previous point
- Development of a tailored automatic controller parameters adaptation based on Bayesian optimization
- Simulation of the developed algorithms on a racing drone
- Test of the algorithms on a real racing drone
The thesis will be in collaboration between UZH Robotics and Perception group and ETH IDSC Intelligent Control Systems group.
[1] Fröhlich, Lukas P., Melanie N. Zeilinger, and Edgar D. Klenske. "Cautious Bayesian optimization for efficient and scalable policy search." Learning for Dynamics and Control. PMLR, 2021.
[2] A. Romero, S. Sun, P. Foehn, and D. Scaramuzza, “Model predictive contouring control for time-optimal quadrotor flight,” IEEE Trans. Robot., doi: 10.1109/TRO.2022.3173711.
Angel Romero Aguilar roagui@ifi.uzh.ch, Andrea Carron, carrona@ethz.ch, Kim Wabersich wkim@ethz.ch
Angel Romero Aguilar roagui@ifi.uzh.ch, Andrea Carron, carrona@ethz.ch, Kim Wabersich wkim@ethz.ch