Theses and Dissertations

Date of Award

5-2024

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Qi Lu

Second Advisor

Emmett Tomai

Third Advisor

Dong-Chul Kim

Abstract

The objective of this research is to establish a fundamental approach to tuning PID (Proportional-Integral-Derivative) parameters for a simulated quadrotor drone. Implementing a PID controller for autonomous flight provides a straightforward and efficient method for monitoring and correcting robotic movement based on the robot's current state. However, applying a PID approach to a quadrotor's flight controller poses challenges, such as assigning multiple parameters to control an inherently under-actuated system. This includes the need to find optimal parameter values that reduce the likelihood of large overshoots and lengthy adjustment times. Ineffectively tuning PID parameters can have detrimental effects on autonomously designed controllers, and manual tuning, while possible, can be a time-consuming and sub-optimal process.

To address these challenges, this research proposes the utilization of Particle Swarm Optimization (PSO) for tuning PID parameters in a simulated quadrotor. The performance of the quadrotor is evaluated using a specific set of PID parameters. The PSO algorithm is employed to find optimal PID values for thrust, yaw, and translational PIDs for x- and y-positions by identifying converging values across randomly created particles. The results demonstrate converging properties for particles that achieve minimal fitness scores, particularly in reducing overshoot. Furthermore, the results indicate that the optimized PID controller outperforms the default PID controller without optimization.

Comments

Copyright 2024 Eric Xavier Rodriguez.

https://go.openathens.net/redirector/utrgv.edu?url=https://www.proquest.com/pqdtglobal1/dissertations-theses/particle-swarm-optimization-training-quadrotor/docview/3085314800/sem-2?accountid=7119

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