Flow Control in Continuous-Time Systems

  • Przemysław Ignaciuk
  • Andrzej Bartoszewicz
Part of the Communications and Control Engineering book series (CCE)


In this chapter, we introduce the fundamental concepts behind the fluid-flow modeling of data traffic in communication networks. We emphasize the effects caused by action delay, which is the time that elapses from the moment the control information (or the controller command) is sent by a network node, the information reaches the data source it is destined for, appropriate action is taken by the source, and until subsequently that action affects the state of the node that issued the command. Indeed, as recognized in many significant papers, for example [3, 6, 7, 10, 14, 17, 22, 26], the existence of action delay constitutes the main obstacle in providing efficient control in data transmission networks, and it should be explicitly considered in the controller design and system analysis. Since we intend to make use of the benefits of SMC, which is well known to be robust and efficient regulation technique successfully applied in many engineering areas (see, e.g., recent special issues [4, 15, 25]), it is of paramount importance to account for the adverse effects of delay. This is due to the fact that delay reduces the system robustness – typically, mismatched perturbations are introduced and the invariance property [9] no longer holds – which threatens stability of the sliding motion. In the design procedures presented in this chapter, we overcome the delay problem by an appropriate selection of the switching function which incorporates a state predictor. In this way, the delay in the feedback loop no longer poses a stability threat, and the system dynamics can be tuned for the maximum responsiveness to the changes of networking conditions reflected in the fluctuations of the available bandwidth.


Queue Length Buffer Capacity Maximum Throughput Switching Function Propose Control Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Przemysław Ignaciuk
    • 1
  • Andrzej Bartoszewicz
    • 2
  1. 1.Institute of Information TechnologyTechnical University of ŁódźŁódźPoland
  2. 2.Institute of Automatic ControlTechnical University of ŁódźŁódźPoland

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