A Modular Framework for Distributed Model Predictive Control of Nonlinear Continuous-Time Systems (GRAMPC-D)
Systems and Control
2020-10-26 v1 Systems and Control
Abstract
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.
Cite
@article{arxiv.2010.12315,
title = {A Modular Framework for Distributed Model Predictive Control of Nonlinear Continuous-Time Systems (GRAMPC-D)},
author = {Daniel Burk and Andreas Völz and Knut Graichen},
journal= {arXiv preprint arXiv:2010.12315},
year = {2020}
}
Comments
Submitted to Optimization and Engineering