Algorithms for Generation and Tracking of Fast and Agile Flight Trajectories
High-speed flight through cluttered environments is essential to many time-sensitive robotics applications. It requires motion planning and flight control algorithms that enable highly accurate maneuvering at the edge of the vehicle’s capability. These algorithms must overcome challenges particular...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/143268 https://orcid.org/0000-0002-3601-0935 |
| Summary: | High-speed flight through cluttered environments is essential to many time-sensitive robotics applications. It requires motion planning and flight control algorithms that enable highly accurate maneuvering at the edge of the vehicle’s capability. These algorithms must overcome challenges particular to fast and agile flight, such as complex dynamics effects including significant unsteady aerodynamics and challenging conditions like post-stall and uncoordinated flight. We propose trajectory generation and tracking algorithms that address these challenges for a quadcopter aircraft and for a fixed-wing transitioning aircraft that combines vertical take-off and landing (VTOL) with efficient forward flight.
This thesis contains several contributions. First, we show that robust control based on incremental nonlinear dynamic inversion (INDI) enables fast and agile flight without depending on an accurate dynamics model. Based on the INDI technique, we design a comprehensive quadcopter flight control algorithm that achieves accurate trajectory tracking without relying on any vehicle aerodynamics model. Second, we show differential flatness of a global nonlinear six-degree-of-freedom (6DOF) flight dynamics model for a tailsitter flying wing transitioning aircraft. We leverage the flat transform to design an INDI flight control algorithm capable of tracking agile aerobatics maneuvers that exploit the entire flight envelope, including post-stall and sideways knife-edge flight. Third, we present a trajectory generation algorithm that aims to identify the actual dynamic feasibility boundary by efficiently combining analytical, numerical, and experimental evaluations in trajectory optimization. Finally, we demonstrate our contributions in fast and agile flight through elaborate experiments. |
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