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Author ORCID Identifier

https://orcid.org/0000-0001-5312-6916

Date Available

7-10-2023

Year of Publication

2022

Document Type

Doctoral Dissertation

Degree Name

Doctor of Philosophy (PhD)

College

Engineering

Department/School/Program

Mechanical Engineering

Faculty

Jesse B. Hoagg

Faculty

Jesse B. Hoagg

Abstract

This dissertation presents new results on multi-agent formation control and applies the new control algorithms to quadrotor unmanned air vehicles. First, this dissertation presents a formation control algorithm for double-integrator agents, where the formation is time varying and the agents’ controls satisfy a priori bounds (e.g., the controls accommodate actuator saturation). The main analytic results provide sufficient conditions such that all agents converge to the desired time-varying relative positions with one another and the leader, and have a priori bounded controls (if applicable). We also present results from rotorcraft experiments that demonstrate the algorithm with time-varying formations and bounded controls. These experimental results include indoor experiments using a motion-capture system as well as outdoor experiments demonstrating the algorithm in a real-world environment with disturbances (e.g., wind) and only onboard feedback. Next, the dissertation extends the formation control method to address agents with damped-rigid-body dynamics (rather than double-integrator dynamics). We also use a barrier-function method to prevent collisions, and enforce state and control constraints. This method modifies the desired formation control as little as possible to enforce the constraints, and is implemented as a quadratic program. We then introduce an observer UAV that uses an onboard vision system and detection algorithm to obtain estimates of a leader trajectory, which can be used as feedback for the formation control. Finally, we present results from outdoor rotorcraft experiments that demonstrate the algorithm with time-varying formations, bounded controls, collision avoidance, and leader tracking.

Digital Object Identifier (DOI)

https://doi.org/10.13023/etd.2022.236

Funding Information

U.S. Department of Agriculture (no.2018-67021-27416) in 2017-2022

National Science Foundation (no. OIA-1849213) in 2020-2022

National Aeronautics and Space Administration through NASA Kentucky Space Grant (no. NNX15AR69H) in 2017-2018

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