A modular framework for distributed model predictive control of nonlinear continuous-time systems (GRAMPC-D)

Language
en
Document Type
Article
Issue Date
2023-04-03
First published
2022-06-01
Issue Year
2022
Authors
Burk, Daniel
Völz, Andreas
Graichen, Knut
Editor
Publisher
Springer US
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 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 and uses the concept of neighbor approximation to enhance convergence speed. The presented framework can be accessed through C++ and Python and also supports plug-and-play and data exchange between agents over a network.

Journal Title
Optimization and Engineering
Volume
23
Issue
2
Citation

Optimization and Engineering 23.2 (2022): S. 771-795. https://link.springer.com/article/10.1007/s11081-021-09605-3

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