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Active queue management algorithm based on data-driven predictive control

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Abstract

Model predictive control (MPC) is a popular strategy for active queue management (AQM) that is able to incorporate physical and user defined constraints. However, the current MPC methods rely on explicit fluid model of TCP behavior with input time delay. In this paper, we propose a novel AQM algorithm based on data-driven predictive control, called Data-AQM. For Internet system with large delay, complex change and bad disturbance, data-driven predictive controller can be obtained directly based on the input–output data alone and does not require any explicit model of the system. According to the input–output data, the future queue length in data buffer, which is the basis of optimizing drop probability, is predicted. Furthermore, considering system constraints, the control requirement is converted to the optimal control objective, then the drop probability is obtained by solving the optimal problem online. Finally, the performances of Data-AQM are evaluated through a series of simulations.

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Acknowledgments

This work is supported by the National Nature Science Foundation of China (No. 61403159), the Jilin Provincial Science Foundation of China (No. 20140520063JH) and the Science and Technology Research Planning Project of the Education Department of Jilin Province (No. 2016431).

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Authors and Affiliations

  1. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, 130025, China

    Ping Wang

  2. Department of Control Science and Engineering, Jilin University, Campus NanLing, Changchun, China

    Ping Wang & Daji Zhu

  3. School of Mechatronic Engineering, Changchun University of Technology, Changchun, China

    Xiaohui Lu

Authors
  1. Ping Wang
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  2. Daji Zhu
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  3. Xiaohui Lu
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Correspondence to Ping Wang.

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Wang, P., Zhu, D. & Lu, X. Active queue management algorithm based on data-driven predictive control. Telecommun Syst 64, 103–111 (2017). https://doi.org/10.1007/s11235-016-0162-6

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  • DOI: https://doi.org/10.1007/s11235-016-0162-6

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