Abstract

Quadcopters are used frequently in various applications around the world. However, the complexity of dynamic behavior of the quadcopter requires advanced stabilizing control for its movement. In addition, precise mathematical modeling for quadcopter is highly challenging task because of its highly non-linear behavior and complex structure. In this research, we identify the dynamical system of the quadcopter using two system Identification techniques. The dynamics includes the 3-movement angles pitch, roll and yaw. The first approach includes the direct identification of the transfer function using TF estimation from the input output data with the help of system identification toolbox in MATLAB. The second technique involves the Neural Network technique where we use feed forward neural Network to train the model first that will later correctly identify models from the data generated by the same model. In all techniques data is generated from Simulink using Pseudo Random Binary Signal (PRBS) and step signal in some dynamics depend upon the complexity of dynamic model.

About the Speaker

Mr. Khubaib Ahmad received his Bachelor’s Degree in Electrical Engineering from the University of Central Punjab, Pakistan, in 2021. Currently, he is pursuing his Master’s degree in Systems and Control Engineering in the Control and Instrument Engineering Department at King Fahd University of Petroleum and Minerals, Saudi Arabia, where he also serves as a Teaching Assistant. His research focuses on Swarm Formation Control, Intelligent Control, Adaptive Control, Optimization Algorithms, and Nonlinear Control.