TU Berlin

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SPP 1914 Cyber-Physical Networking

NICCI^2 - Network-Informed Control – Control-Informed network: towards multi techNology dynamICally ChangIng networks

Institutions

  • TU Berlin
  • Paderborn University
  • Otto-von-Guericke University Magdeburg

Team @ CCS

Funding

  • DFG (Deutsche Forschungsgemeinschaft)

Project Time

  • 01/2020 - 12/2022

Description

The opportunities provided by feedback control of networked dynamical systems are enormous. However, it is by no means clear how to harness modern wireless communication, network and computation technologies to develop high-performance cyber-physical systems. The main stumbling blocks stem from the significant gaps which exist between understanding of constituent parts and the challenges faced when bringing them together. One of the key complications is that the traffic demands of the control loops is often considered independently from the communication capabilities. In current setups, the control system has to simply cope with the quality of service provided by the network. Whilst this facilitates an independent design of communication and control systems, the achievable performance is limited. Hence, mostly slow and non safety-critical tasks are currently performed via wireless communication.

These limitations could be overcome by an efficiently integrated overall optimization of control and communication systems. Unfortunately, a practical solution to this optimization problem is infeasible due to the complexity and distributed nature of the overall system. We strive instead for a modular solution. Controllers and network resource management are designed separately, yet exchange information and interact collaboratively. This exchange comprises predictions of control requirements, communication capabilities and technological choices. The developed algorithms at both the control and communication resource management sides, leverage predictions and learn from their respective actions.

Within the first funding phase of this project, we analyzed limits of achievable performance of this class of networked cyber-physical systems. Using suitable deterministic and also stochastic frameworks, we were able to establish minimum requirements on the network, which are necessary to achieve the desired quality of control. We further proposed a number of control-aware scheduling and network-aware control methods wherein information between network manager and controller is exchanged sporadically. Our insights motivate numerous interesting and relevant research questions to be investigated within a second funding period.

In the second funding phase of the project, we aim to further elucidate fundamental tradeoffs and develop modular approaches for the design of networked cyber-physical systems. We will focus on practical cases where heterogeneous and complementary wireless communication technologies with time and state-dependent properties are available. Integrating such technologies into possibly large-scale cyber-physical systems with feedback is highly non-trivial. To be successful, we will fuse channel predictions, machine learning and estimation techniques with optimization-based methods for scheduling and control. We will exemplarily test and refine our algorithms on networked vehicle systems and industry 4.0 test scenarios with robots.

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