Dynamic modeling and control of a quadrotor using nonlinear approaches based on MEMS sensor's experimental data

This paper presents the control of a quadrotor using nonlinear approaches based on the experimentally measured sensors data. The main goal is the control and closed loop simulation of a quadrotor using feedback linearization and sliding mode algorithms. First, a nonlinear model of quadrotor is derived using Newton-Euler equations. To have a more realistic simulation the sensors noise performance were measured using a setup. sensors data was measured under on engines. Since the experimental data for sensor had error and noise, a Kalman filter was used to reduce sensors noise effect. Results demonstrate good performance for Kalman filter and controllers. Results showed that feedback linearization and sliding mode controllers performance was good but angles changes were smoother on feedback linearization controller. With increasing uncertainty, feedback linearization performance was away desired mode from this aspect The time to reach the goal situation while increasing uncertainty was no significant impact on the performance of sliding mode controller.Thus feedback linearization controller added PID is Appropriate to Maintain the quadrotor attitude while sliding mode controller has better performance to angles change and transient situations.