Learning Dynamics for Improving Control of Overactuated Flying Systems
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Author / Producer
Date
2020-10
Publication Type
Journal Article
ETH Bibliography
yes
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Abstract
Overactuated omnidirectional flying vehicles are capable of generating force and torque in any direction, which is important for applications such as contact-based industrial inspection. This comes at the price of an increase in model complexity. These vehicles usually have non-negligible, repetitive dynamics that are hard to model, such as the aerodynamic interference between the propellers. This makes it difficult for high-performance trajectory tracking using a model-based controller. This letter presents an approach that combines a data-driven and a first-principle model for the system actuation and uses it to improve the controller. In a first step, the first-principle model errors are learned offline using a Gaussian Process (GP) regressor. At runtime, the first-principle model and the GP regressor are used jointly to obtain control commands. This is formulated as an optimization problem, which avoids ambiguous solutions present in a standard inverse model in overactuated systems, by only using forward models. The approach is validated using a tilt-arm overactuated omnidirectional flying vehicle performing attitude trajectory tracking. The results show that with our proposed method, the attitude trajectory error is reduced by 32% on average as compared to a nominal PID controller. © 2020 IEEE.
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Publication status
published
External links
Editor
Book title
Journal / series
Volume
5 (4)
Pages / Article No.
5283 - 5290
Publisher
IEEE
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Aerial Systems: Mechanics and Control; Model Learning for Control
Organisational unit
03737 - Siegwart, Roland Y. / Siegwart, Roland Y.