Citation
Hooper, Meredith Leigh (2025) Machine-Learned Propulsion Strategies: From Adaptive Damage Compensation to Advanced Aeromobility. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/79aa-ja50. https://resolver.caltech.edu/CaltechTHESIS:06042025-002343274
Abstract
Autonomous vehicles are regularly sent into "dull, dirty, and dangerous" environments where the risk of damage is high. Avoidance or mitigation of such damage is therefore paramount to maintain effective autonomy. In this thesis, we use machine learning to investigate two different propulsive strategies that may be used by autonomous vehicles. The first, flapping propulsion, shows remarkable ability in nature to recover from damage simply by altering stroke kinematics. Using machine learning, we ask whether and how such mitigation of damage would be possible for a robotic autonomous vehicle. The second propulsive strategy we investigate is single-rotor propulsion, most commonly seen in helicopters. With this system, we seek to avoid damage before it occurs by improving mobility and control authority via thrust vectoring.
In Part I, we use an evolutionary strategy (CMA-ES) with hardware-in-the-loop to explore optimal machine-learned adaptations to propulsor damage. Experimental function evaluations are performed by a flexible propulsor actuated by a spherical parallel manipulator (SPM). The machine-learned forces and trajectory parameters are compared to in vivo observations in order to determine whether bio-inspired strategies to adapt to significant propulsor damage are the most efficient, or whether they may be affected by irrelevant evolutionary pressures. With amputation of approximately 50% of the propulsor, we find that a complete recovery in thrust production and fitness is made. Some characteristics of the recovered trajectory are similar to natural swimmers, while others differ. Recovery when producing side-force is even more complex. Not all trials are able to recover force production and fitness, and no clear strategy to modify amplitude or frequency is seen. We conclude Part I by using PIV measurements to detail the effect of compensatory strategies on hydrodynamics. Both amputated and intact trajectories clearly show utilization of a drag-based paddling strategy, but the hydrodynamics of the intact and amputated fins differ significantly. This suggests that the machine-learned trajectories are not simply reestablishing the same wake as the intact fin to achieve the same thrust and fitness.
Given the success in applying machine learning in-the-loop to a complex propulsive system where fluid-structure interactions are significant, we utilize the same strategy in Part II to begin to explore helicopter aeromechanics. We built an independent blade control (IBC) system that interfaces with the CMA-ES algorithm to explore optimal blade pitch trajectories. Using this platform, we explore two preliminary optimizations designed to vector thrust; the first, for sustained thrust vectoring that might be utilized upon takeoff or landing, and the second, for short-time thrust vectoring that could be used for enhanced maneuverability. We present some preliminary results from these optimizations and lay out a foundation for future applications of this experimental system.
Item Type: | Thesis (Dissertation (Ph.D.)) | ||||||||
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Subject Keywords: | aerodynamics, hydrodynamics, machine learning, experimental fluid dynamics, flapping propulsion, helicopters, rotorcraft, particle image velocimetry | ||||||||
Degree Grantor: | California Institute of Technology | ||||||||
Division: | Engineering and Applied Science | ||||||||
Major Option: | Aeronautics | ||||||||
Awards: | Donald Coles Prize in Aeronautics, 2025. Rolf D. Buehler Memorial Award in Aeronautics, 2021. | ||||||||
Thesis Availability: | Restricted to Caltech community only | ||||||||
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Group: | GALCIT | ||||||||
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Defense Date: | 29 May 2025 | ||||||||
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Record Number: | CaltechTHESIS:06042025-002343274 | ||||||||
Persistent URL: | https://resolver.caltech.edu/CaltechTHESIS:06042025-002343274 | ||||||||
DOI: | 10.7907/79aa-ja50 | ||||||||
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Default Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||
ID Code: | 17406 | ||||||||
Collection: | CaltechTHESIS | ||||||||
Deposited By: | Meredith Hooper | ||||||||
Deposited On: | 05 Jun 2025 17:55 | ||||||||
Last Modified: | 17 Jun 2025 18:01 |
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