An Enhanced Model Predictive Controller for Quadrotor Attitude Quick Adjustment with Input Constraints and Disturbances

In this paper, a fuzzy model predictive controller based on disturbance observer is proposed for attitude control of quadrotor Unmanned Aerial Vehicle (UAV) subject to input constraints and disturbances. The proposed algorithm consists of a fuzzy predictive controller based on a T-S fuzzy model and a disturbance observer. FMPC can handle the constraints of the system and disturbance observer is designed to compensate the disturbance effect. In this work, the Takagi-Sugeno fuzzy model is used for the predictive controller, which is used to more approximate the nonlinear model to obtain a faster convergence speed. To test the effectiveness of the proposed algorithm, simulations for the quadrotor are implemented and the tracking performance among the proposed method, existing linear predictive controller and PID algorithm is compared with each other. Both simulation and experiment results show the effectiveness of using fuzzy model and disturbance observer.