SUMMARY

Multi-UAV cooperative lift systems have the potential to dramatically alleviate the logistical burden of aerial payload transportation missions from a scalability and portability standpoint. Unlike traditional single-vehicle transportation solutions, targeted delivery of a large variety of payloads may be achieved by distributing the load among several inexpensive UAVs, rather than maintaining a large inventory of aerial vehicle platforms with prohibitive logistical cost or lift capacity. In order to carry out a large span of mission sets, these systems must be capable of reliably stabilizing mid-flight when arranged in variable geometric configurations with unknown system parameters. This dissertation expands upon a previously developed modular docking system to account for multi-agent self-assembly and focuses on novel adaptive flight control enabled by extended Kalman filter parameter estimation for highly variable multi-UAV system configurations. High-fidelity models and simulation results are presented, leveraging techniques including multibody feedback linearization constraints and constraint-based impulse contact models in order to capture complex dynamics, validate adaptive control strategies, and optimize mechanical designs. This dissertation additionally presents experimental results for cooperative UAV docking, and multi-UAV flight control and parameter estimation.