Model-based guidance and control of tail-sitter transitioning unmanned aerial systems

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Authors
McIntosh, Kristoff, Francis
Issue Date
2024-05
Type
Electronic thesis
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en_US
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Mechanical engineering
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Abstract
Tail-sitter unmanned aerial systems (UAS) are vehicles that are capable of operating in and switching between the vertical take-off and landing (VTOL) and fixed-wing flight regimes through a 90 degree rigid body rotation. This capability lends tail-sitter UAS several unique characteristics and abilities that allow them to outperform pure VTOL or fixed-wing UAS in a variety of civil and military applications. The key design challenge regarding achieving autonomy for tail-sitter UAS lies in the characterization of the transition flight regime, or the flight regime between VTOL and fixed-wing flight. The complexity of the transition regime makes it particularly difficult to design unified guidance (path-planning) and control architectures for tail-sitter UAS that are applicable to all flight regimes (VTOL, fixed-wing, and transition), with typical approaches to control relying on either avoiding or overpowering the aerodynamics of the transition regime. This can result in overly conservative controllers capable of only stabilizing a tail-sitter under very specific conditions. Furthermore, guidance methods for tail-sitter UAS rarely consider the aerodynamics of the transition regime, often relying on heuristic or geometric methods for generating state trajectories for various flight missions. To address these challenges, this thesis aims to develop a unified approach to a guidance and control for tail-sitter UAS autonomy. Specific considerations are given to (1) tail-sitter path-planning methodologies that rely on transition model-based optimization techniques used to generate missions that can fulfill a specific objective (specified by the user) as well as aerodynamic state estimations for the wings that are useful for controller design, (2) model-based control methodologies that strategically use any wing aerodynamic state estimates generated from mission planning to improve position and attitude tracking performance, and (3) the derivation of an analytical guarantee of controller stability that both shows the existence of a convergence region of position and velocity error states and estimates the size said convergence region based on a error-state dependent bound on the uncertainty in the aerodynamic forces estimate and maximum moment. All contributions are validated in the context of controlling a tail-sitter configuration known as a quadrotor biplane tail-sitter.
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May2024
School of Engineering
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Rensselaer Polytechnic Institute, Troy, NY
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