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The purpose of this paper is to present fault tolerant control of a quadrotor based on the enhanced proportional integral derivative (PID) structure in the presence of one or more actuator faults.

Mathematical model of the quadrotor is derived by parameter identification of the system for the simulation of the UAV dynamics and flight control in MATLAB/Simulink. An improved PID structure is used to provide the stability of the nonlinear quadcopter system both for attitude and path control of the system. The results of the healty system and the faulty system are given in simulations, together with motor dynamics.

In this study, actuator faults are considered to show that a robust controller design handles the loss of effectiveness in motors up to some extent. For the loss of control effectiveness of 20 per cent in first and third motors, psi state follows the reference with steady state error, and it does not go unstable. Motor 1 and Motor 3 respond to given motor fault quickly. When it comes to one actuator fault, steady state errors remain in some states, but the system does not become unstable.

In this paper, an enhanced PID controller is proposed to keep the quadrotor stable in case of actuator faults. Proposed method demonstrates the effectiveness of the control system against motor faults.

The purpose of this paper is to present an improved particle filter-based attitude estimator for a quadrotor unmanned aerial vehicle (UAV) that addresses the degeneracy issues.

Control of a quadrotor is not sufficient enough without an estimator to eliminate the noise from low-cost sensors. In this work, particle filter-based attitude estimator is proposed and used for nonlinear quadrotor dynamics. But, since recursive Bayesian estimation steps may rise degeneracy issues, the proposed scheme is improved with four different and widely used resampling algorithms.

Robustness of the proposed schemes is tested under various scenarios that include different levels of uncertainty and different particle sizes. Statistical analyses are conducted to assess the error performance of the schemes. According to the statistical analysis, the proposed estimators are capable of reducing sensor noise up to 5x, increasing signal to noise ratio up to 2.5x and reducing the uncertainty bounds up to 36x with root mean square value of as low as 0.0024, mean absolute error value of 0.036, respectively.

To the best of the authors’ knowledge, the originality of this paper is to propose a robust particle filter-based attitude estimator to eliminate the low-cost sensor errors of quadrotor UAVs.