TY - GEN
T1 - Controller-Aware Dynamic Network Management for Industry 4.0
AU - Balta, Efe C.
AU - Mamduhi, Mohammad H.
AU - Lygeros, John
AU - Rupenyan, Alisa
PY - 2022/12/9
Y1 - 2022/12/9
N2 - In this paper, we consider a cyber-physical manufacturing system (CPMS) scenario containing physical components (robots, sensors, and actuators), operating in a digitally connected, constrained environment to perform industrial tasks. The CPMS has a centralized control plane with digital twins (DTs) of the physical resources, computational resources, and a network manager that allocates network resources. Existing approaches for the allocation of network resources are typically fixed with respect to controller-dependent run-time specifications, which may impact the performance of physical processes. We propose a dynamic network management framework, where the network resource allocation schemes are controller-aware. The information about the controllers of the physical resources is implemented at the DT level, and metrics, such as regret bounds, take the process performance measures into account. The proposed network management schemes optimize physical system performance by balancing the shared resources between the physical assets on the plant floor, and by considering their control requirements, providing a new perspective for dynamic resource allocation. A simulation study is provided to illustrate the performance of the proposed network management approaches and compare their resource allocation performance efficiencies.
AB - In this paper, we consider a cyber-physical manufacturing system (CPMS) scenario containing physical components (robots, sensors, and actuators), operating in a digitally connected, constrained environment to perform industrial tasks. The CPMS has a centralized control plane with digital twins (DTs) of the physical resources, computational resources, and a network manager that allocates network resources. Existing approaches for the allocation of network resources are typically fixed with respect to controller-dependent run-time specifications, which may impact the performance of physical processes. We propose a dynamic network management framework, where the network resource allocation schemes are controller-aware. The information about the controllers of the physical resources is implemented at the DT level, and metrics, such as regret bounds, take the process performance measures into account. The proposed network management schemes optimize physical system performance by balancing the shared resources between the physical assets on the plant floor, and by considering their control requirements, providing a new perspective for dynamic resource allocation. A simulation study is provided to illustrate the performance of the proposed network management approaches and compare their resource allocation performance efficiencies.
KW - Measurement
KW - Service robots
KW - System performance
KW - Process control
KW - Dynamic scheduling
KW - Robot sensing systems
KW - Sensor systems
U2 - 10.1109/IECON49645.2022.9968460
DO - 10.1109/IECON49645.2022.9968460
M3 - Conference contribution
SN - 9781665480260 (PoD)
T3 - Proceedings of the Annual Conference of the IEEE Industrial Electronics Society
BT - IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society
PB - IEEE
T2 - IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society
Y2 - 17 October 2022 through 20 October 2022
ER -