Unified NMPC framework for attitude and position control for a VTOL UAV

Abstract

In this article, a parameterized nonlinear model predictive control strategy is developed for trajectory tracking and stabilization of a quadrotor unmanned aerial vehicle. The control strategy handles structurally with constraints on commands and input derivatives and deals with model uncertainties and white noise on whole state vector. Moreover, the resulting parametrization decreases the number of decision variables related to the nonlinear optimization problem substantially, which highly facilitates the attainment of the optimal control sequence. The proposed nonlinear model predictive control framework also enables the use of different nonlinear formulations of the unmanned aerial vehicle whatever the complexity of the model being used. Simulation results for different flight conditions are presented in order to show the efficiency of the tracking performance and highlight the advantages of the proposed control scheme.