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Neural network approximation of model predictive controller for congestion control of TCP/AQM networks

Marami, B ; Sharif University of Technology | 2007

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  1. Type of Document: Article
  2. DOI: 10.1109/ICCAS.2007.4406804
  3. Publisher: IEEE Computer Society , 2007
  4. Abstract:
  5. Due to the excellent properties of the model predictive controllers (MPC) in implementing on nonlinear and time varying systems, utilizing these controllers as Active Queue Management (AQM) strategy is proposed for congestion control of computer networks. However, high computational demand to solve the optimization problem exist in these controllers is a major obstacle when they are applied on fast large-scale constrained systems such as the computer networks. Small signal linearized model of the nonlinear TCP/AQM network is used to design MPC controller and then a neural network is trained to approximate the model predictive control strategy. Using this approach, due to the parallel processing property of the neural networks, some of the computations can be done in parallel form, therefore, computational effort is reduced compared to the commonly used MPC schemes. The performance of the proposed 'controller in desired queue tracking and disturbance rejection is compared with two well-known AQM methods, PI and RED. © ICROS
  6. Keywords:
  7. Computer control systems ; Computer networks ; Congestion control (communication) ; Constrained optimization ; Disturbance rejection ; Model predictive control ; Neural networks ; Optimization ; Predictive control systems ; Queueing networks ; Queueing theory ; Time varying systems ; Transmission control protocol ; Active Queue Management ; Computational demands ; Computational effort ; Constrained systems ; Model predictive controllers ; Neural network approximation ; Optimization problems ; Parallel processing ; Controllers
  8. Source: International Conference on Control, Automation and Systems, ICCAS 2007, Seoul, 17 October 2007 through 20 October 2007 ; 2007 , Pages 2591-2596 ; 8995003871 (ISBN); 9788995003879 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/4406804