scholarly journals Suboptimal RED Feedback Control for Buffered TCP Flow Dynamics in Computer Network

2007 ◽  
Vol 2007 ◽  
pp. 1-17 ◽  
Author(s):  
N. U. Ahmed ◽  
X. H. Ouyang
Keyword(s):  
Feedback Control ◽  
Computer Network ◽  
Optimal Feedback Control ◽  
Packet Losses ◽  
Quadratic Polynomials ◽  
Link Utilization ◽  
Stochastic Point Process ◽  
The Status ◽  
Feedback Laws ◽  
Recursive Search

We present an improved dynamic system that simulates the behavior of TCP flows and active queue management (AQM) system. This system can be modeled by a set of stochastic differential equations driven by a doubly stochastic point process with intensities being the controls. The feedback laws proposed monitor the status of buffers and multiplexor of the router, detect incipient congestion by sending warning signals to the sources. The simulation results show that the optimal feedback control law from the class of linear as well as quadratic polynomials can improve the system performance significantly in terms of maximizing the link utilization, minimizing congestion, packet losses, as well as global synchronization. The optimization process used is based on random recursive search technique known as RRS.

scholarly journals Suboptimal feedback control of TCP flows in computer network using random early discard (RED) mechanism

2005 ◽  
Vol 2005 (5) ◽  
pp. 477-489 ◽  
Author(s):  
N. U. Ahmed ◽  
Cheng Li
Keyword(s):  
Feedback Control ◽  
Dynamic Model ◽  
Computer Network ◽  
Control Laws ◽  
Doubly Stochastic ◽  
Polynomial Type ◽  
Stochastic Point Process ◽  
Proposed Model ◽  
Recursive Search ◽  
Suboptimal Feedback Control

We consider a dynamic model that simulates the interaction of TCP sources with active queue management system (AQM). We propose a modified version of an earlier dynamic model called RED. This is governed by a system of stochastic differential equations driven by a doubly stochastic point process with intensity as the control. The feedback control law proposed observes the router (queue) status and controls the intensity by sending congestion signals (warnings) to the sources for adjustment of their transmission rates. The (feedback) control laws used are of polynomial type (including linear) with adjustable coefficients. They are optimized by use of genetic algorithm (GA) and random recursive search (RRS) technique. The numerical results demonstrate that the proposed model and the method can improve the system performance significantly.

Tips on Stochastic Optimal Feedback Control and Bayesian Spatiotemporal Models: Applications to Robotics

2014 ◽  
Vol 137 (3) ◽  
Author(s):  
Jongeun Choi ◽  
Dejan Milutinović
Keyword(s):  
Sensor Networks ◽  
Feedback Control ◽  
Control Theory ◽  
Mobile Sensor Networks ◽  
Optimal Feedback Control ◽  
Mobile Sensor ◽  
Optimal Feedback ◽  
Spatiotemporal Models ◽  
Feedback Control Theory

This tutorial paper presents the expositions of stochastic optimal feedback control theory and Bayesian spatiotemporal models in the context of robotics applications. The presented material is self-contained so that readers can grasp the most important concepts and acquire knowledge needed to jump-start their research. To facilitate this, we provide a series of educational examples from robotics and mobile sensor networks.

scholarly journals An Optimal Feedback Control Strategy for Nonlinear, Distributed-Parameter Processes

Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 758 ◽  
Author(s):  
Debaprasad Dutta ◽  
Simant Ranjan Upreti
Keyword(s):  
Feedback Control ◽  
Oil Recovery ◽  
Control Strategy ◽  
Moving Boundary ◽  
State Feedback Control ◽  
Optimal Feedback Control ◽  
Distributed Parameter ◽  
Optimal State ◽  
Non Linear ◽  
Feedback Control Strategy

In this work, an optimal state feedback control strategy is proposed for non-linear, distributed-parameter processes. For different values of a given parameter susceptible to upsets, the strategy involves off-line computation of a repository of optimal open-loop states and gains needed for the feedback adjustment of control. A gain is determined by minimizing the perturbation of the objective functional about the new optimal state and control corresponding to a process upset. When an upset is encountered in a running process, the repository is utilized to obtain the control adjustment required to steer the process to the new optimal state. The strategy is successfully applied to a highly non-linear, gas-based heavy oil recovery process controlled by the gas temperature with the state depending non-linearly on time and two spatial directions inside a moving boundary, and subject to pressure upsets. The results demonstrate that when the process has a pressure upset, the proposed strategy is able to determine control adjustments with negligible time delays and to navigate the process to the new optimal state.

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