  The growing potential of quadcopters in various domains, such as aerial
photography, search and rescue, and infrastructure inspection, underscores the
need for real-time control under strict safety and operational constraints.
This challenge is compounded by the inherent nonlinear dynamics of quadcopters
and the on-board computational limitations they face. This paper aims at
addressing these challenges. First, this paper presents a comprehensive
procedure for deriving a linear yet efficient model to describe the dynamics of
quadrotors, thereby reducing complexity without compromising efficiency. Then,
this paper develops a steady-state-aware Model Predictive Control (MPC) to
effectively navigate quadcopters, while guaranteeing constraint satisfaction at
all times. The main advantage of the steady-state-aware MPC is its low
computational complexity, which makes it an appropriate choice for systems with
limited computing capacity, like quadcopters. This paper considers Parrot Bebop
2 as the running example, and experimentally validates and evaluates the
proposed algorithms.
