Efficient Propulsion for Versatile Unmanned Aerial Vehicles: Studies in Mechanics and Control

Abstract

This thesis presents a control algorithm for significantly enhancing the available thrust and minimizing the required electrical power consumption of a Variable-Pitch Propulsion (VPP) system, where the VPP system is made up of a brushless DC motor and a variable-collective-pitch propeller with its own servo motor. The variable-collective-pitch propeller mechanism has received recent attention because of the mechanism’s capability to enhance thrust response bandwidth and propulsive efficiency compared to conventional Unmanned Aerial Vehicle (UAV) propulsion systems with rigid-geometry propellers; the mechanism has this capability due to a second mechanical degree of freedom in the propeller geometry, allowing the collective pitch angle of the propeller blades to vary according to actuation from a servo motor. When paired with a properly designed control algorithm, the motor speed and pitch angle can be tuned in real time to track prescribed thrust trajectories while satisfying some optimality condition. Motivation for research into highly efficient VPP propulsion systems is encouraged by the intense interest from private and public sectors in UAVs that are capable of Vertical TakeOff and Landing (VTOL); while generally capable of both fixed-wing and hovering flight, VTOL UAVs with rigid-geometry propellers often exhibit short flight time due to non-optimal propulsion system efficiency across-the-board. Prior research into power-minimizing control strategies for small VPP systems has been targeted at multi-rotor platforms and has thus made assumptions that limit variation in the speed of propeller inflow and in the magnitude of thrust, thus limiting the technology’s applicability to VTOL platforms. The control algorithm presented in this thesis is designed to accommodate for the wide range of air inflow speeds and thrust magnitudes through the following algorithm components: a linear feedback thrust controller with a nonlinear, adaptive feedforward thrust model derived from Blade Element Momentum propeller theory; an estimator to tune the thrust feedforward model parameters in real-time; and an Extremum Seeking algorithm for tracking the minimum-power control input configuration. Analysis of controller performance is discussed with reference to simulated and physical validation experiments.