Student research highlights: Quadrotor electronics and intelligent control

Abstract

Unmanned aerial vehicles (UAVs) have seen diverse applications, including radiation or border surveillance, with quadrotors offering small size, relatively low cost, high mobility, mass production, and easy access to challenging environments. In addition, quadrotors can carry sensors, cameras, or other gadgets for information collection, data fusion, and subsequent actions of targets, as was exemplified during the Fukushima nuclear plant disaster, by visual and sensory surveillance or for an air delivery system. UAVs have also spurred extensive research on physical design, operation principles, components, and control algorithms, partially due to complex stabilization problems, including the Stanford testbed of autonomous rotorcraft for multiagent control [1] quadrotor with linear controllers for position control systems, the X-4 flyer [2] with discrete model controllers, and the MikroKopter [3] with an open-source code development, as well as implementation and control with state estimation. Inherently nonlinear UAV dynamics can also be governed by intelligent controllers that can handle complex, nonlinear systems for superior outcomes. As an intelligent framework, fuzzy logic (FL) reflects real-life vagueness and imprecise data to provide decisions via nonlinear mapping via fuzzy sets, rules, and membership functions [4]. This project describes the development and implementation of a practical, low cost, lightweight quadrotor electronic test platform design to evaluate the FL closed-loop controller for roll and pitch stabilization.