  The modular open-source framework GRAMPC-D for model predictive control of
distributed systems is presented in this paper. The modular concept allows to
solve optimal control problems (OCP) in a centralized and distributed fashion
using the same problem description. It is tailored to computational efficiency
with the focus on embedded hardware. The distributed solution is based on the
Alternating Direction Method of Multipliers (ADMM) and uses the concept of
neighbor approximation to enhance convergence speed. The presented framework
can be accessed through Cpp and Python and also supports plug-and-play and data
exchange between agents over a network.
