TY  - CHAP
A1  - Altherr, Lena
A1  - Ederer, Thorsten
A1  - Lorenz, Ulf
A1  - Pelz, Peter F.
A1  - Pöttgen, Philipp
ED  - Lübbecke, Marco E.
ED  - Koster, Arie
ED  - Letmathe, Peter
ED  - Madlener, Reihard
ED  - Preis, Britta
ED  - Walther, Grit
T1  - Designing a feedback control system via mixed-integer programming
T2  - Operations Research Proceedings 2014: Selected Papers of the Annual International Conference of the German Operations Research
N2  - Pure analytical or experimental methods can only find a control strategy for technical systems with a fixed setup. In former contributions we presented an approach that simultaneously finds the optimal topology and the optimal open-loop control of a system via Mixed Integer Linear Programming (MILP). In order to extend this approach by a closed-loop control we present a Mixed Integer Program for a time discretized tank level control. This model is the basis for an extension by combinatorial decisions and thus for the variation of the network topology. Furthermore, one is able to appraise feasible solutions using the global optimality gap.
KW  - Optimal Topology
KW  - Controller Parameter
KW  - Level Control System
KW  - Technical Operation Research
KW  - Optimal Closed Loop
Y1  - 2016
SN  - 978-3-319-28695-2
U6  - http://dx.doi.org/10.1007/978-3-319-28697-6_18
SP  - 121
EP  - 127
PB  - Springer
CY  - Cham
ER  - 
TY  - JOUR
A1  - Altherr, Lena
A1  - Ederer, Thorsten
A1  - Lorenz, Ulf
A1  - Pelz, Peter F.
A1  - Pöttgen, Philipp
ED  - Lübbecke, Marco
ED  - Koster, Arie
ED  - Letmathe, Peter
ED  - Madlener, Reihard
ED  - Peis, Britta
ED  - Walther, Grit
T1  - Experimental validation of an enhanced system synthesis approach
JF  - Operations Research Proceedings 2014
N2  - Planning the layout and operation of a technical system is a common task
for an engineer. Typically, the workflow is divided into consecutive stages: First,
the engineer designs the layout of the system, with the help of his experience or of
heuristic methods. Secondly, he finds a control strategy which is often optimized
by simulation. This usually results in a good operating of an unquestioned sys-
tem topology. In contrast, we apply Operations Research (OR) methods to find a
cost-optimal solution for both stages simultaneously via mixed integer program-
ming (MILP). Technical Operations Research (TOR) allows one to find a provable
global optimal solution within the model formulation. However, the modeling error
due to the abstraction of physical reality remains unknown. We address this ubiq-
uitous problem of OR methods by comparing our computational results with mea-
surements in a test rig. For a practical test case we compute a topology and control
strategy via MILP and verify that the objectives are met up to a deviation of 8.7%.
Y1  - 2014
SN  - 978-3-319-28695-2
U6  - http://dx.doi.org/10.1007/978-3-319-28697-6_1
PB  - Springer
CY  - Basel
ER  -