Using an evolutionary algorithm to search for control algorithm parameters with a quadrocopter as hardware in the loop

This thesis describes the process of finding control loop parameters for a quadrocopter using machine learning techniques and a hardware in the loop evaluation method. A quadrocopter is an Unattended Aerial Vehicle (UAV) with four rotors that can be controlled individually to steer the vehicle. The purpose of the control loop is to keep the roll, pitch and yaw-angles of the quadrocopter to a predefined value by regulating the thrust of each rotor. A quadrocopter is designed and manufactured and a control algorithm is implemented on the built in controller board. An Evolutionary Algorithm (EA) is implemented on a Personal Computer (PC) and an automated search for control parameters using hardware in the loop with the quadrocopter as evaluation model is performed. The results show that a quadrocopter can be used together with hardware in the loop to improve the control algorithm parameters