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This paper deals with the development of an adaptive robust controller for an Unmanned Aircraft System with variable mass. The goal is to improve trajectory tracking and energy performance while the aircraft mass is gradually reducing due to payload release. To improve the trajectory tracking performance, we propose an algorithm that merges the concepts of least squares method and sliding mode observer for the estimation of the vehicle mass. The nonlinear observer estimates the linear velocities and accelerations, which are further used to obtain the vehicle mass. A robust adaptive pole placement controller based on the Attractive Ellipsoid Method uses this estimation to update the controllers gains due to the mass variation. To validate the effectiveness of the proposed algorithm for performing aerial transportation and deployment of payloads, we present a numerical example comparing the performance of the proposed method with respect to using Proportional Derivative controllers as well as Robust Controllers. KEYWORDSload transportation, model in the loop, nonlinear observer, robust adaptive control, UAS, variable mass 1 INTRODUCTION Diverse research works have explored original solutions for the Unmanned Aircraft System (UAS)-based payload transportation problem for constant and variable mass payloads. As a few examples, the authors in Kui et al. [1]developed an autonomous vehicle to transport a payload by using a tensor embedded in the vehicle, while the authors in Geng and Langelaan [2] designed strategies for load transportation that involves a team of cooperative vehicles. Previously, the authors in Haus et al. [3] presented the MORUS project where they conducted the