  This article proposes a novel control architecture using a centralized
nonlinear model predictive control (CNMPC) scheme for controlling multiple
micro aerial vehicles (MAVs). The control architecture uses an augmented state
system to control multiple agents and performs both obstacle and collision
avoidance. The optimization algorithm used is OpEn, based on the proximal
averaged Newton type method for optimal control (PANOC) which provides fast
convergence for non-convex optimization problems. The objective is to perform
position reference tracking for each individual agent, while nonlinear
constrains guarantee collision avoidance and smooth control signals. To produce
a trajectory that satisfies all constraints a penalty method is applied to the
nonlinear constraints. The efficacy of this proposed novel control scheme is
successfully demonstrated through simulation results and comparisons, in terms
of computation time and constraint violations, while are provided with respect
to the number of agents.
