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.
