Bioinspired backstepping sliding mode control and adaptive sliding innovation filter of quadrotor unmanned aerial vehicles

Abstract

Quadrotor unmanned aerial vehicles have become the most commonly used flying robots with wide applications in recent years. This paper presents a bioinspired control strategy by integrating the backstepping sliding mode control technique and a bioinspired neural dynamics model. The effects of both disturbances and system and measurement noises on the quadrotor unmanned aerial vehicle control performance have been addressed in this paper. The proposed control strategy is robust against disturbances with guaranteed stability proven by the Lyapunov stability theory. In addition, the proposed control strategy is capable of providing smooth control inputs under noises. Considering the modeling uncertainties, the adaptive sliding innovation filter is integrated with the proposed control to provide accurate state estimates to improve tracking effectiveness. Finally, the simulation results demonstrate that the proposed control strategy provides satisfactory tracking performance for a quadrotor unmanned vehicle operating under disturbances and noises.