Modular distributed model predictive control of nonlinear systems with neighborhood models

Third party funded individual grant

Project Details

Project leader:
Prof. Dr.-Ing. Knut Graichen

Contributing FAU Organisations:
Lehrstuhl für Regelungstechnik

Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH) (2.6 - Sachbeihilfen)
Start date: 01/04/2019
End date: 31/12/2020

Abstract (technical / expert description):

The steadily growing demands on efficiency and flexibility of modern automation and control systems requires a broader design approach for the overall system that goes beyond the isolated look at and control of single subsystems. Decentral and distributed control schemes follow this holistic design approach by including the interdependencies between the subsystems in the control design.

Model predictive control (MPC) appears to be a suitable control approach to tackle these kind of systems. In essence, MPC relies on the numerical solution of a finite-horizon dynamic optimization problem that is repetitively solved according to the sampling rate of the system. An extension of MPC to coupled systems is distributed MPC (DMPC), which assigns a single communicating MPC agent to each subsystem. 

The goal of the project is to develop a DMPC scheme for nonlinear coupled systems, where each MPC agent contains a neighborhood model that anticipates the dynamical behavior of its neighbors in order to enhance the convergence and robustness of the distributed algorithm. Besides the development and mathematical investigation of the methodology, a further goal of the project is the numerical and experimental realization of the control approach. A particular intention of the project is to develop a modular framework that allows for an easy configuration and adaptation of the coupling structure for suitable system classes. 

Last updated on 2019-02-05 at 12:46