Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Daniel Wilczak - Junior Dylan Ballback - Sophomore Jack Nguyen - Senior Rachel Secord - Phd Student
Lead Presenter's Name
Daniel Wilczak
Faculty Mentor Name
Dr Matthew Verleger
Abstract
The purpose of the present study was to design a flight control system with no pre-determined mathematical model, but instead using a genetic algorithm to maintain the optimal altitude. The study is done through a quantitative empirical research method. In the process of conducting the research, we found that programming a genetic algorithm was cumbersome for novice users to implement. Due to this, we created and released an open-source Python package called EasyGA.
An initial population of 15 chromosomes and 100 generations were used during the trial. The throttle value of the device had an associated gene value of 1 second. When the trial of hundred generations was completed, machine learning was achieved. Please refer to the graph for greater understanding. Preliminary results showed that optimizing a one DOF device, in real-time, is possible without using a pre-determined mathematical model.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
Yes, Ignite Grant
Optimizing a One DOF Robot Without a Mathematical Model Using aGenetic Algorithm
The purpose of the present study was to design a flight control system with no pre-determined mathematical model, but instead using a genetic algorithm to maintain the optimal altitude. The study is done through a quantitative empirical research method. In the process of conducting the research, we found that programming a genetic algorithm was cumbersome for novice users to implement. Due to this, we created and released an open-source Python package called EasyGA.
An initial population of 15 chromosomes and 100 generations were used during the trial. The throttle value of the device had an associated gene value of 1 second. When the trial of hundred generations was completed, machine learning was achieved. Please refer to the graph for greater understanding. Preliminary results showed that optimizing a one DOF device, in real-time, is possible without using a pre-determined mathematical model.