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In 2007, the Technical Standard for Port and Harbor Facilities in Japan was revised and established as the calculation method for the requirement of channel radius of curvature in category 2. However, the validation of many existing channels has been done later. The authors studied channel radius of curvature on the above calculation method of category 2 and analyzed automatic identification system (AIS) track data at existing Japanese typical bend approach channels. Beside, the authors studied the channel bent shape and layout of buoys using vessel maneuvering simulation.

This paper presents two models for representing the ship's tracking dynamic behavior. The first model is a hybrid model combined by a state-dependent nonlinear time series model and a ship moving equation model, which has the advantage in long-term prediction. The second model is a pure state-dependent time series model, which has the advantage in short-term prediction. Both of them showed better simulation results than the model presented in Peng et al. [2010] did.

In recent years, as a measure to reduce the effect of global warming caused by the maritime transport, vessels have been strongly advised to reduce fuel consumption. Weather Routing (WR) is a relatively inexpensive and easy way to implement the solution. However, for good operation, WR requires a good estimate of the vessel's propulsive performance at seas. In previous studies, it had been shown that ship's performance can be estimated by analyzing abstract logbook data. In this paper, the authors proposed a method for estimating the vessel's propulsive performance using thrust data obtained from an onboard monitoring system and using several possible parameterable methods. For carrying out the study, an Onboard Monitoring System was fitted onboard a 4,430TEU container ship for data collection. A minute examination of the collected data showed a clear relation between ocean wave state and thrust. A set of formulas was established to calculate the thrust increase relying on wave height and direction. The obtained equation allowed the authors to reconstruct an actual voyage within a 3% margin of error, showing the good performance of the proposed estimation method. WR simulations showed also the potential reduction of fuel consumption reduction achievable using the estimated vessel performances.

For actualization of ship tracking control along a desired path with a constant velocity, a hybrid model is proposed to represent the ship's tracking dynamic behavior. Firstly, a single-input single-output nonlinear time series model is built for characterizing the responses between the ship's heading angle deviation and its rudder angle. To represent nonlinearity of the ship motion, the rolling angle is used as the model index to make the model parameters vary with the ship sailing states considering the yaw-heel-effect. The nonlinear time series model is identified offline by using previously observed real data. Then, a state-space model combined with the relationship between the heading angle deviations and the cross track errors is proposed to represent the tracking dynamic behavior. On the basis of the identified state-space type tracking motion model, a predictive controller is designed to steer the ship sailing forward with the constant velocity along the predefined reference path. The effectiveness of the nonlinear time series model-based method for the tracking control proposed in this paper is demonstrated by simulation studies and actual experiments.

For a class of smooth nonlinear multivariable systems whose working-points vary with time and the future working-points knowledge are unknown, a combination of a local linearization and a polytopic uncertain linear parameter-varying (LPV) state-space model is built to approximate the present and the future system’s nonlinear behavior, respectively. The combination models are constructed on the basis of a matrix polynomial multi-input multi-output (MIMO) RBF-ARX model identified offline for representing the underlying nonlinear system. A min–max robust MPC strategy is designed to achieve the systems’ output-tracking control based on the approximate models proposed. The closed loop stability of the MPC algorithm is guaranteed by the use of time-varying parameter-dependent Lyapunov function and the feasibility of the linear matrix inequalities (LMIs). The effectiveness of the modeling and control methods proposed in this paper is illustrated by a case study of a thermal power plant simulator.

Monitoring and clustering of ship and main engine motions are urgent problems in order to realize energy saving navigation. In this paper, authors represent their motions by statistical autoregressive model using minimum AIC procedure. The modelling proceeds applying locally stationary fitting procedure. The watch officers and operators can automatically detect the change of ship's states by monitoring the spectra gained from the fitted model and detect the outlier, missing values of the data and predict the motions using Kalman filtering procedure. The most important feature of this system is to automatically cluster the ship's time series and save the results to the database. The watch officers and operators can analyse the past ship's and main engine's states and determine the next operating method of the navigation including the gain scheduling the ship's control system, for examples, autopilot, the tracking system, ship's governor system and so on.

本論文は，著者が1967年に東京商船大学に奉職以来の研究論文について，年代順にその要旨を述べたものである。内容的には，船舶の横揺れの非線形性を高次バイスペクトルで論じた論文，船舶の操運動の自己回帰モデルを用いた統計的解析，オートパイロット制御，舵減揺制御，舶用ガバナ制御等の確率的最適制御に関する論文，線形可変ゲイン法を用いる船舶の最適トラッキング制御法に関する論文，非線形最適化手法を用いる最短時間制御に関する論文などのレビューを含む。, This paper represents the review and prospect on the author's research works during 40 years since 1967 when the author started his researching life at the Tokyo University of Mercantile Marine. The fields which the author has took strong interests in, are mainly the time series analysis and stochastic control of ship's behavior at irregular sea, the tracking problem and the minimum time berthing problem of ships. In this paper, those researching works are shown using the actual experimental results., 東京海洋大学大学院海洋科学技術研究科

This paper will outline the various stages in the design and development of advanced rudder roll stabilization system based high slew rate steering system. The slew rate means rudder angle change rate. First, this paper introduces a high slew rate steering system with Electric hydraulic system (EHS). The basic concept of EHS system is to combine the hydraulic power source system and actuator system with controlling the speed of electric motor of hydraulic pump depending on the condition required to the actuator. Next, this paper presents the adaptive rudder roll stabilization system based on a multivariate auto-regressive model that is called Multivariate Auto-regressive Rudder roll Control System (MARCS). Finally, conclusions and discussions based on full scales experiments are summarized.

In the previous chapter, we presented three types of autopilot system based on the stationary linear AR model. However, actual sea conditions may change gradually or abruptly due to various factors, and in such a situation, we must consider a nonstationary time series. Furthermore, it may become necessary to consider the nonlinear response of the ship, which will be of particular importance to tracking control. In this chapter, we propose extensions of our statistical optimal controller based on the locally stationary AR model and the RBF-ARX model and develop a noise-adaptive autopilot and a path-tracking autopilot.

A critical problem in applying the optimal control theory to a large-scale complex system that is subject to large disturbances, such as a ship, an electric power plant, or a large chemical plant, is that it is sometimes difficult to obtain a precise state-space model from the theory of the subject area. For such systems, Akaike, H.: Ann. Inst. Stat. Math. 23, 163–180 (1971) proposed the ARX model for the identification of the controlled system. In this chapter, we propose a method for obtaining optimal control laws based on the linear stationary state-space model of the controlled system for two performance criteria. Based on this linear quadratic optimal controller, we develop three types of optimal controllers for a ship: an AR model-based autopilot, a roll reduction control system, and an engine governor control system.

The features of dynamic phenomena can be described using time series models. In this chapter, we present various types of autoregressive models for the analysis of time series, such as univariate and multivariate autoregressive models, an autoregressive model with exogenous variables, a locally stationary autoregressive model, and a radial basis function autoregressive model. Various tools for analyzing dynamic systems such as the impulse response function, the power spectrum, the characteristic roots, and the power contribution are obtained through these models (Akaike and Nakagawa 1989; Kitagawa 2010).

In this paper, a new minimum-time ship maneuvering system using neural network and nonlinear model predictive compensator is proposed. In the proposed method, the neural network is used for interpolating the pre-computed minimum-time solution for the real-time situation and the non linear dynamical model of the ship is used for compensating the control error caused by some modeling errors, disturbances etc. In order to investigate the effectiveness of the proposed method, computer simulations and actual sea tests are carried out using a training ship Shioji Maru .

It is a great concerning problem for a mariner to know how maneuvering method is the best for his ship. Moreover it is also important for a ship builder in the meaning how the designed ship shows the maneuvering performance in full conditions. A minimum time maneuvering yields an answer about this problem. The minimum time maneuvering problem of ship can be formulated as two point boundary problem. The author has solved this non linear problem using the Sequential Conjugate Gradient-Restoration Algorithm (SCGRA) by Wu and Miel. This numerical method was a powerful solving method. However, a tedious programming method was needed. In this paper, the author proposes to treat this problem in a non linear programming formulation. According to this, our problem is transformed to more easy one and we can solve the transformed one by using a general Non Linear Programming (NLP) package. In this paper, the author solves a minimum time changing course problem and a Williamson's turning (man over board) one which are described by non linear mathematical maneuvering model.