Fault Tolerant Controller Design for Multirotor Systems

Abstract

Purpose: The use area of Unmanned Aerial Vehicles (UAVs) is increasing day by day. Especially Quadcopters are applicable for wide range of potential applications such as security, traffic surveillance, natural risk management, environmental exploration, agriculture and military. However, engine failures, sensor failures or communication failures may occur in quadcopters and it cause both serious accidents in vehicles and damage the environment or surroundings. The purpose of this study aims to develop a fault-tolerant controller design for a quadcopter to enhance its reliability and stability in the presence of component failures or malfunctions. Methodology: The proposed fault-tolerant controller design involves the following key steps: Quadrotor System Design: The mathematical position equations of the quadrotor unmanned aerial vehicle to be designed in the MATLAB Simulink program are extracted using the Euler rotation formulation. Model design is done in MATLAB Simulink by determining the dynamic equations. The designed model also includes the effects of external disturbances and possible failures. Fault Detection: Proposed system has a closed loop control system in order to detect the anomalies in the sensors, motors and critical communication modules. After realizing the anormalities, a fault identification algorithm determines the specific components responsible for the observed anormal behavior. In this study, motors failures and abnormalities are examined. Fault Isolation: In order to prevent motors having faulty behaviors from distorted signals, PID feedback control system is proposed. Quadcopter roll, pitch, yawn movements are tested by reference signal. Originality: Proposed PID controller has ability to fix errors under disturbances; however, nonlinear controller methods have better ability to control the control system. The proportional controller is dependent on the current error, the integral controller is the sum of past errors and the derivative controller is an estimation of future errors. When we tune the PID controller values, we can achieve better state responses but curve settlement time increases. On the other hand, when we decrease the curve settlement time, we observed more aggressive system behavior. For more robust system error isolation, nonlinear quadcopter system will be tested with nonlinear controllers, such as sliding mode control.