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Related Experiment Video

Updated: May 25, 2026

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

Nonconvex integer optimal robust impulsive control strategy for first-order piecewise finite precision nonlinear

Charlotte Yuk-Fan Ho¹, Bingo Wing-Kuen Ling, Herbert H C Iu

¹School of Mathematical Sciences, Queen Mary, University of London, Mile End Road, London E1 4NS, UK. c.ho@qmul.ac.uk

ISA Transactions

|January 24, 2012

Summary

This summary is machine-generated.

This study introduces a new model for the average queue size in random early detection (RED) algorithms. A robust control strategy is proposed to stabilize queue size effectively and efficiently.

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Last Updated: May 25, 2026

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

Area of Science:

Control Systems Engineering
Computer Networks
Nonlinear Dynamics

Background:

Random Early Detection (RED) algorithms are widely used for congestion control in networks.
Characterizing and controlling the average queue size in RED is crucial for network performance.
Existing models may not fully capture the complexities of finite precision nonlinear dynamics.

Purpose of the Study:

To develop a novel first-order piecewise finite precision nonlinear dynamical model for RED average queue size.
To design a robust impulsive control strategy for stabilizing the average queue size.
To minimize control power while ensuring stability and adherence to theoretical queue size.

Main Methods:

Development of a first-order piecewise finite precision nonlinear dynamical model.
Formulation of a nonconvex integer optimal robust impulsive control strategy.
Computer numerical simulations to validate the control strategy's effectiveness.

Main Results:

The proposed model accurately characterizes the average queue size dynamics.
The impulsive control strategy effectively stabilizes the average queue size.
Simulations demonstrate the efficiency and effectiveness of the proposed control approach.

Conclusions:

The developed nonlinear dynamical model provides a valuable tool for analyzing RED algorithms.
The robust impulsive control strategy offers an efficient solution for queue size stabilization.
This research contributes to improved network congestion control through advanced modeling and control techniques.