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This book presents multivariate time series methods for the
analysis and optimal control of feedback systems. Although ships'
autopilot systems are considered through the entire book, the
methods set forth in this book can be applied to many other
complicated, large, or noisy feedback control systems for which it
is difficult to derive a model of the entire system based on theory
in that subject area. The basic models used in this method are the
multivariate autoregressive model with exogenous variables (ARX)
model and the radial bases function net-type coefficients ARX
model. The noise contribution analysis can then be performed
through the estimated autoregressive (AR) model and various types
of autopilot systems can be designed through the state-space
representation of the models. The marine autopilot systems
addressed in this book include optimal controllers for
course-keeping motion, rolling reduction controllers with rudder
motion, engine governor controllers, noise adaptive autopilots,
route-tracking controllers by direct steering, and the reference
course-setting approach. The methods presented here are exemplified
with real data analysis and experiments on real ships. This book is
highly recommended to readers who are interested in designing
optimal or adaptive controllers not only of ships but also of any
other complicated systems under noisy disturbance conditions.
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