Over the past decades, although stochastic system control has been
studied intensively within the field of control engineering, all the
modelling and control strategies developed so far have concentrated on
the performance of one or two output properties of the system. such as
minimum variance control and mean value control. The general assumption
used in the formulation of modelling and control strategies is that the
distribution of the random signals involved is Gaussian. In this book, a
set of new approaches for the control of the output probability density
function of stochastic dynamic systems (those subjected to any bounded
random inputs), has been developed. In this context, the purpose of
control system design becomes the selection of a control signal that
makes the shape of the system outputs p.d.f. as close as possible to a
given distribution. The book contains material on the subjects of: -
Control of single-input single-output and multiple-input multiple-output
stochastic systems; - Stable adaptive control of stochastic
distributions; - Model reference adaptive control; - Control of
nonlinear dynamic stochastic systems; - Condition monitoring of bounded
stochastic distributions; - Control algorithm design; - Singular
stochastic systems.
A new representation of dynamic stochastic systems is produced by using
B-spline functions to descripe the output p.d.f. Advances in
Industrial Control aims to report and encourage the transfer of
technology in control engineering. The rapid development of control
technology has an impact on all areas of the control discipline. The
series offers an opportunity for researchers to present an extended
exposition of new work in all aspects of industrial control.
