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Time Series Modeling for Analysis and Control

Advanced Autopilot and Monitoring Systems

  • Book
  • © 2015

Overview

Authors:
  1. Kohei Ohtsu

    1. Ohtsu Maritime Institute, Co. Ltd., Japan

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  2. Hui Peng

    1. School of Information Sci & Eng, Central South University, Zuojialong, Changsha, China

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    You can also search for this author in PubMed   Google Scholar

  3. Genshiro Kitagawa

    1. Research Org. of Info. and Systems, Minato-ku, Japan

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  • Presents a practical time series method for designing various type of optimal controllers of very complex, large, and noisy systems
  • Shows how various types of commercial autopilot systems have been already developed based on a practical time series method
  • Provides a practical statistical method of analyzing feedback systems
  • Includes supplementary material: sn.pub/extras

Part of the book series: SpringerBriefs in Statistics (BRIEFSSTATIST)

Part of the book sub series: JSS Research Series in Statistics (JSSRES)

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Table of contents (4 chapters)

  1. Front Matter

    Pages i-ix
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  2. Introduction

    • Kohei Ohtsu, Hui Peng, Genshiro Kitagawa
    Pages 1-6

  3. Time Series Analysis Through AR Modeling

    • Kohei Ohtsu, Hui Peng, Genshiro Kitagawa
    Pages 7-56

  4. Design of a Model-Based Autopilot System for Course Keeping Motion

    • Kohei Ohtsu, Hui Peng, Genshiro Kitagawa
    Pages 57-81

  5. Advanced Autopilot Systems

    • Kohei Ohtsu, Hui Peng, Genshiro Kitagawa
    Pages 83-115

  6. Back Matter

    Pages 117-119
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Keywords

About this book

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.

Authors and Affiliations

  • Ohtsu Maritime Institute, Co. Ltd., Japan

    Kohei Ohtsu

  • School of Information Sci & Eng, Central South University, Zuojialong, Changsha, China

    Hui Peng

  • Research Org. of Info. and Systems, Minato-ku, Japan

    Genshiro Kitagawa

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