End To End Adaptive Congestion Control In Tcp Ip Networks
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End-to-End Adaptive Congestion Control in TCP/IP Networks
This book provides an adaptive control theory perspective on designing congestion controls for packet-switching networks. Relevant to a wide range of disciplines and industries, including the music industry, computers, image trading, and virtual groups, the text extensively discusses source-oriented, or end-to-end, congestion control algorithms. The book empowers readers with clear understanding of the characteristics of packet-switching networks and their effects on system stability and performance. It provides schemes capable of controlling congestion and fairness and presents real-world applications to demonstrate the modeling and control techniques.
A Generalized Framework of Linear Multivariable Control
Author: Liansheng Tan
language: en
Publisher: Butterworth-Heinemann
Release Date: 2017-02-04
A Generalized Framework of Linear Multivariable Control proposes a number of generalized models by using the generalized inverse of matrix, while the usual linear multivariable control theory relies on some regular models. The book supports that in H-infinity control, the linear fractional transformation formulation is relying on the inverse of the block matrix. If the block matrix is not regular, the H-infinity control does not apply any more in the normal framework. Therefore, it is very important to relax those restrictions to generalize the classical notions and models to include some non-regular cases. This book is ideal for scholars, academics, professional engineer and students who are interested in control system theory. - Presents a comprehensive set of numerical procedures, algorithms, and examples on how to deal with irregular models - Provides a summary on generalized framework of linear multivariable control that focuses on generalizations of models and notions - Introduces a number of generalized models by using the generalized inverse of matrix
Nonlinear Control of Dynamic Networks
Significant progress has been made on nonlinear control systems in the past two decades. However, many of the existing nonlinear control methods cannot be readily used to cope with communication and networking issues without nontrivial modifications. For example, small quantization errors may cause the performance of a "well-designed" nonlinear control system to deteriorate. Motivated by the need for new tools to solve complex problems resulting from smart power grids, biological processes, distributed computing networks, transportation networks, robotic systems, and other cutting-edge control applications, Nonlinear Control of Dynamic Networks tackles newly arising theoretical and real-world challenges for stability analysis and control design, including nonlinearity, dimensionality, uncertainty, and information constraints as well as behaviors stemming from quantization, data-sampling, and impulses. Delivering a systematic review of the nonlinear small-gain theorems, the text: Supplies novel cyclic-small-gain theorems for large-scale nonlinear dynamic networks Offers a cyclic-small-gain framework for nonlinear control with static or dynamic quantization Contains a combination of cyclic-small-gain and set-valued map designs for robust control of nonlinear uncertain systems subject to sensor noise Presents a cyclic-small-gain result in directed graphs and distributed control of nonlinear multi-agent systems with fixed or dynamically changing topology Based on the authors’ recent research, Nonlinear Control of Dynamic Networks provides a unified framework for robust, quantized, and distributed control under information constraints. Suggesting avenues for further exploration, the book encourages readers to take into consideration more communication and networking issues in control designs to better handle the arising challenges.