Real-time embedded sliding mode observer for quadcopter UAVs

Sliding mode observer (SMO) as a nonlinear and robust observer, is believed to be able to provide all required states information for control process of quadcopter UAVs. In this paper, a comparative assessment through numerical simulation is conducted between SMO and Extended Kalman Filter (EKF) to demonstrate the performance of both estimators. The results obtained demonstrate good performance of SMO in dealing with noise and uncertainty. Furthermore, experiments are carried out to validate the performance of SMO in real-time. The results show that estimated states can track true states fast with small estimation steady state errors and the observer estimates the unmeasured states smoothly.