Nonlinear Stochastic Systems with Incomplete Information, 1st Edition

  • Published By:
  • ISBN-10: 1447149149
  • ISBN-13: 9781447149149
  • DDC: 003.76
  • Grade Level Range: College Freshman - College Senior
  • 248 Pages | eBook
  • Original Copyright 2013 | Published/Released May 2014
  • This publication's content originally published in print form: 2013

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Nonlinear Stochastic Processes addresses the frequently-encountered problem of incomplete information. The causes of this problem considered here include: missing measurements; sensor delays and saturation; quantization effects; and signal sampling. Divided into three parts, the text begins with a focus on H∞ filtering and control problems associated with general classes of nonlinear stochastic discrete-time systems. Filtering problems are considered in the second part, and in the third the theory and techniques previously developed are applied to the solution of issues arising in complex networks with the design of sampled-data-based controllers and filters. Among its highlights, the text provides: • a unified framework for filtering and control problems in complex communication networks with limited bandwidth; • new concepts such as random sensor and signal saturations for more realistic modeling; and • demonstration of the use of techniques such as the Hamilton–Jacobi–Isaacs, difference linear matrix, and parameter-dependent matrix inequalities and sums of squares to handle the computational challenges inherent in these systems. The collection of recent research results presented in Nonlinear Stochastic Processes will be of interest to academic researchers in control and signal processing. Graduate students working with communication networks with lossy information and control of stochastic systems will also benefit from reading the book.

Table of Contents

Front Cover.
Half Title Page.
Title Page.
Copyright Page.
1: Introduction.
2: Quantized H∞ Control for Time-Delay Systems with Missing Measurements.
3: H∞ Filtering with Missing Measurements and Randomly Varying Sensor Delays.
4: Filtering with Randomly Occurring Nonlinearities, Quantization, and Packet Dropouts.
5: H∞ Filtering with Randomly Occurring Sensor Saturations and Missing Measurements.
6: Distributed H∞-Consensus Filtering in Sensor Networks.
7: Distributed H∞ Filtering for Polynomial Systems in Sensor Networks.
8: Sampled-Data Approach to Distributed H∞ Filtering in Sensor Networks.
9: Sampled-Data Synchronization Control and State Estimation for Complex Networks.
10: Bounded H∞ Synchronization and State Estimation for Complex Networks.
11: Conclusions and Future Work.