Nonlinear Model Predictive Control using State Estimation based on Unscented Kalman Filter for Quadcopters

Abstract

This paper examines the fault tolerant control problem of a quadcopter with failed sensors. In the case where all state variables of control system are not exactly known, the control system needs to incorporate some type of state estimation. This paper applies the state estimation method based on Unscented Kalman Filter (UKF) to the observer system of a quadcopter with limited output sensors. Model predictive control (MPC) is a well-established control method in which the current control input is obtained by solving a finite-horizon open-loop optimal control problem using the current state of the system as the initial state, and this procedure is repeated at each sampling instant. The objective of this study is to establish a control method for a quadcopter with limited measurable state variables by means of incorporating state estimation into model predictive control.