ESKF-based Model Predictive Control for a Quadrotor Subject to Wind Disturbances and Measurement Noise

Abstract

In this study, a model predictive control (MPC) approach based on the extended state observer (ESO) is proposed for solving the tracking control problem of quadrotor helicopter with wind disturbances. To handle the quadrotor’s measurement noise, the gain of ESO is optimized timely to generate the extended state based Kalman filter (ESKF). First, the quadrotor dynamic model considered the aerodynamic effects of lateral wind is built by the Newton-Euler approach. Then, based on the quadrotor’s dynamic model subject to the wind disturbances, ESKF-based MPC controllers are designed for position tracking and stabilization of rotational movements. In addition, for the attitude control, the nonlinear dynamics are linearized by the feedback linearization strategy. The ESKF is used for estimating the quadrotor state and wind disturbances which can be compensated in the control inputs. Finally, the simulation results illustrate that the proposed controller effectively force the quadrotor track the reference trajectory in the presence of wind disturbances and measurement noise.