Nonlinear Control and Filtering for Stochastic Networked Systems

By: Lifeng Ma, Zidong Wang, Yuming Bo

Write A Review

eText | 7 December 2018 | Edition Number 1

At a Glance

Format
ePUB

eText

$115.50

or 4 interest-free payments of $28.88 with

Instant online reading in your Booktopia eTextbook Library *

Read online on

Desktop

Tablet

Mobile

Not downloadable to your eReader or an app

Why choose an eTextbook?

Instant Access *

Purchase and read your book immediately

Read Aloud

Listen and follow along as Bookshelf reads to you

Study Tools

Built-in study tools like highlights and more

* eTextbooks are not downloadable to your eReader or an app and can be accessed via web browsers only. You must be connected to the internet and have no technical issues with your device or browser that could prevent the eTextbook from operating.

In this book, control and filtering problems for several classes of stochastic networked systems are discussed. In each chapter, the stability, robustness, reliability, consensus performance, and/or disturbance attenuation levels are investigated within a unified theoretical framework. The aim is to derive the sufficient conditions such that the resulting systems achieve the prescribed design requirements despite all the network-induced phenomena. Further, novel notions such as randomly occurring sensor failures and consensus in probability are discussed. Finally, the theories/techniques developed are applied to emerging research areas.

Key Features

Unifies existing and emerging concepts concerning stochastic control/filtering and distributed control/filtering with an emphasis on a variety of network-induced complexities
Includes concepts like randomly occurring sensor failures and consensus in probability (with respect to time-varying stochastic multi-agent systems)
Exploits the recursive linear matrix inequality approach, completing the square method, Hamilton-Jacobi inequality approach, and parameter-dependent matrix inequality approach to handle the emerging mathematical/computational challenges
Captures recent advances of theories, techniques, and applications of stochastic control as well as filtering from an engineering-oriented perspective
Gives simulation examples in each chapter to reflect the engineering practice