Time Series Modeling for Analysis and Control: Advanced Autopilot and Monitoring Systems (SpringerBriefs in Statistics / JSS Research Series in Statistics)

Ohtsu, Kohei; Peng, Hui; Kitagawa, Genshiro

ISBN 10: 4431553029 / ISBN 13: 9784431553021

Published by Springer, 2015

About this Item

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Seller Inventory # 26372175655

Bibliographic Details

• Title: Time Series Modeling for Analysis and Control: Advanced Autopilot and Monitoring Systems (SpringerBriefs in Statistics / JSS Research Series in Statistics)
• Publisher: Springer
• Publication Date: 2015
• Language: English
• ISBN 10: 4431553029
• ISBN 13: 9784431553021
• Binding: Soft cover
• Condition: New

About this title

Synopsis

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.