Abstract
In recent years, using Unmanned Arial Vehicles (UAV) like quadcopter in civilian and military fields are increased dramatically. Performance and robustness are the most important specifications required for most applications. Different sensors are usually used for a quadcopter to provide the necessary measured states (attitude and position) for control. The white noise generated by physical sensors is one of the important issues that affect the quality of states measurements. The available solutions are still have limited performance for a wide range of nonlinearity. In this paper, Unscented Kalman Filter (UKF) is proposed as a robust estimator that has the ability to work efficiently with high nonlinear systems. Modified PID (PI-D) controller which has better properties than traditional PID controller is used with proposed filter in order to get better performance of quadcopter. The obtained results are compared with that of Extended Kalman Filter (EKF) and proved to be more reliable. Moreover, the results show that the proposed filter largely decreases the error generated by noise and improves the performance of quadcopter better than the EKF.
Highlights
The use of the unmanned aerial vehicles (UAV), such as the quadcopter, has recently increased in various civilian areas because of its advantages, vertical takeoff, landing in a small area and hovering
After several iterations by matlab simulink platform, the results show that Unscented Kalman Filter (UKF) improves the performance of quadcopter better than Extended Kalman Filter (EKF)
In this paper, we addressed the problem of error in measured states caused by generated noise from physical sensors of quadcopter
Summary
The use of the unmanned aerial vehicles (UAV), such as the quadcopter, has recently increased in various civilian areas because of its advantages, vertical takeoff, landing in a small area and hovering. The KF is designed especially for linear systems while the quadcopter is nonlinear system This filter does not work properly in the nonlinear behavior of quadcopter and this lead to push the quadcopter out of control, while in the approach of [5], the authors uses Extended Kalman Filter (EKF) to estimate actual states of quadcopter from noisy measured states. The equations below represent the relations between desired linear accelerations with respect to the inertial frame, and the desired related angles roll, pitch and yaw with respect to the body frame [13]:. Thereafter, the sigma points are put through the nonlinear model equations to obtain predicted states. The following equations are used to calculate the sigma points for the predicted states [19], [21]:. Is the scaling factor which tells how far from the mean we should choose our sigma points, is the covariance matrix of predicted states, th column of the square root weighted covariance [19]
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