Ship Autopilot Research Articles

Application of Fuzzy Pattern Recognition in Comprehensive Evaluation of Ship Navigation System Performance

Chen, L., 2020. Application of fuzzy pattern recognition in comprehensive evaluation of ship navigation system performance. In: Bai, X. and Zhou, H. (eds.), Advances in Water Resources, Environmental Protection, and Sustainable Development. Journal of Coastal Research, Special Issue No. 115, pp. 196-198. Coconut Creek (Florida), ISSN 0749-0208.With the increasing demand for navigation, more and more navigation equipments are applied to ships. Modern ship navigation systems are generally based on a variety of signal processing, which can improve navigation accuracy and reduce marine environmental interference. The Integrated Navigation System (INS) provides optimized integrated navigation information for ships. The core equipment of INS is the ship autopilot with track keeping and heading keeping functions. Through comprehensive performance evaluation and optimal design, the ship navigation system developed has the advantages of advanced performance index, high technical performance, economic and reasonable development funds, etc. The integrated navigation system transmits all kinds of navigation equipment information to the computer through the network, uses the information fusion technology to process synthetically, obtains the best navigation information of the ship, and then displays the comprehensive navigation situation of the ship in real time and dynamically through the electronic chart. Based on this, an integrated navigation system based on fuzzy pattern recognition is designed and analyzed.

An efficient ship autopilot design using observer-based model predictive control

An efficient model predictive control design for ship autopilot, which is a representative marine application, is proposed based on projection neural network in this article. Ship motion control at sea exhibits the characteristics of large inertia, strong nonlinearity, and large delay; furthermore, it is frequently influenced by the external disturbances, leading to a complex uncertain problem. In addition, the amplitude of control input—the rudder is constrained. Given the mechanism of on-line computing and the advantages of handling constraints, the model predictive control is one of the most favorable solutions for this problem. Nevertheless, the major challenge of the implementation of traditional model predictive control in application is the computation intensity. In this article, the capability of parallel computation of projection neural network is employed to optimize the objective function formulated by traditional model predictive control method, aiming to improve the computational efficiency. The overall information of ship motion is normally difficult to be obtained; therefore, a state observer should be also included. Extensive studies are conducted to illustrate the effectiveness of the proposed control design.

Observer-Based Adaptive Fuzzy Control for Intelligent Ship Autopilot with Input Saturation

In this paper, the design problem of intelligent ship autopilot with unmeasured yaw rate and input saturation is investigated based on adaptive fuzzy control. First, an adaptive fuzzy output feedback controller is proposed, then, to improve the convergence speed of the tracking error in the sense of finite time, a finite-time adaptive fuzzy output feedback controller is also designed to improve the control performance. By employing fuzzy logic system (FLS) to estimate the unknown nonlinear function, an adaptive fuzzy state observer is designed to estimate the unmeasured state. An adaptation auxiliary signal for input saturation is also established to compensate the mismatch between the controller signal and the actuator signal. Combining state observer model with backstepping method, two different adaptive fuzzy output-feedback controllers with composite parameter adaptive laws are constructed, respectively. Based on Lyapunov theory, it is proved that all signals in the closed-loop systems are bounded. Finally, the simulation results are conducted to demonstrate the effectiveness of the proposed schemes.

Adaptive modeling of maritime autonomous surface ships with uncertainty using a weighted LS-SVR robust to outliers

Maritime Autonomous Surface Ships (MASS) have become increasingly interesting for the commercial maritime sectors as an alternative to conventional ships. For the purpose of development of MASS operations such as motion control (e.g. collision avoidance, trajectory tracking), the ship dynamic model is of significant importance for technique tests, i.e. verification and validation. The ship dynamic model should be suitable for cases which means the sufficient balance between the complexity and the accuracy of the model. This contribution is aiming to develop a robust ship dynamics modeling approach by making efforts on determining a common ship dynamic model and designing a robust identification method. A response model, i.e. the first-order nonlinear Nomoto model widely-used in ship autopilot design is selected. A robust optimal identification method named optimal DW-LSSVR is proposed by taking advantages of least square support vector regression algorithm (LS-SVR), robust 3α principle (D), adaptive weight technique (W), and artificial bee colony algorithm (ABC for optimization). The robust 3α principle has the function of outlier detection. Adaptive weight technique can adaptively weight support vectors to improve the sparsity of LS-SVR. Furthermore, ABC is served as the structural parameters optimizer for LS-SVR. The ships studied in this work are considered as more realistic objects with uncertain parameters induced by unmodeled terms, variation of the loading condition as well as environmental disturbances, and measurement noises. In identifying the widely-used response model for ships, the optimal DW-LSSVR method is verified and validated on both experimental measurements and simulated data. In simulation tests, the effectiveness of the proposed method in identifying the response model has been demonstrated with the use of simulated data including ones with uncertainties which are generated by activating a response model with predefined parameter values by means of step inputs in Monte Carlo simulations. Besides the simulation tests, the experimental investigation on a real Unmanned Surface Vessel (USV) using experimental zigzag maneuvers also indicates the consistency of the proposed approach.

Optimal universal controllers for roll stabilization

Roll stabilization is an important problem of ship motion control. This problem becomes especially difficult if the same set of actuators (e.g. a single rudder) has to be used for roll stabilization and heading control of the vessel, so that the roll stabilizing system interferes with the ship autopilot. Finding the “trade-off” between the concurrent goals of accurate vessel steering and roll stabilization usually reduces to an optimization problem, which has to be solved in presence of an unknown wave disturbance. Standard approaches to this problem (loop-shaping, LQG, H∞-control etc.) require to know the spectral density of the disturbance, considered to be a “colored noise”. In this paper, we propose a novel approach to optimal roll stabilization, approximating the disturbance by a polyharmonic signal with known frequencies yet uncertain amplitudes and phase shifts. Linear quadratic optimization problems in presence of polyharmonic disturbances can be solved by means of the theory of universal controllers developed by V.A. Yakubovich. An optimal universal controller delivers the optimal solution for any uncertain amplitudes and phases. Using Marine Systems Simulator (MSS) Toolbox that provides a realistic vessel's model, we compare our design method with classical approaches to optimal roll stabilization. Among three controllers providing the same quality of yaw steering, OUC stabilizes the roll motion most efficiently.

An energy-efficient nonlinear robust track keeping control algorithm for the Maritime Silk Road

This paper proposes a nonlinear robust track keeping control algorithm for energy efficiency from the perspective of ship motion control, based on the great significance of the twenty-first Century Maritime Silk Road. Replacing the course error with the driven by the sine function in course keeping loop to serve as the input of the controller, the simulation results tested on the ship autopilot simulation platform show that the controller output (maximum rudder angle) decreases, and the control effect of track keeping is better than the previous one in aspects of energy efficiency and rudder shake inhibition. Otherwise, the proposed nonlinear track keeping control algorithm still works in relatively severe sea states. Substituting the nonlinear feedback for the linear feedback has the generality of control effect improving.

Unscented Kalman Filter trained neural network control design for ship autopilot with experimental and numerical approaches

In the recent decades, the application and research of unmanned surface vessels are experiencing considerable growth, which have caused the demands of intelligent autopilots to grow along with the ever-growing requirements. In this study, the design of an autopilot based on Unscented Kalman Filter (UKF) trained Radial Basis Function Neural Networks (RBFNN) was presented. In particular, in order to provide satisfactory control performance for surface vessels with random external disturbances, the modified UKF was utilised as the weights training mechanism for the RBFNN based controller. The configurations of the newly developed free running scaled model, as well as the online signal processing method, were introduced to enable the experimental studies. The experimental and numerical tests were carried out through using the physical scaled model and corresponding mathematical model to validate the capability of the designed control system under various sailing conditions. The results indicated that the UKF RBFNN based autopilot satisfied the functionalities of course keeping, course changing and trajectory tracking only using the rudder as the actuator. It was concluded that the developed control scheme was effective to track the desired states and robust against unpredictable external disturbances. Moreover, in comparison with Back-Propagation (BP) RBFNN and Proportional-Derivative (PD) based autopilots, the UKF RBFNN based autopilot has the comparable capability in the aspects of providing smooth and effective control laws.

Adaptive Sliding Mode Control for Ship Autopilot with Speed Keeping

Abstract The paper addresses an important issue in surface vessel motion control practice that the ship dynamics and sailing performance can be affected by speed loss. The vessel speed is significantly decreased by the added resistance generated by waves. An adaptive sliding mode course keeping control design is proposed which takes into account uncertain ship dynamics caused by forward speed variations, while avoiding performance compromises under changing operating and environmental conditions. The sliding mode control provides robust performance for time-varying wave disturbances and time-varying changes in ship parameters and actuator dynamics. After combining the unknown but bounded system uncertainties, the design of the adaptation law is obtained which is based on the Lyapunov’s direct method. Simulations on a ship with two rudders illustrate the effectiveness of the proposed solution.

Design a Ship Autopilot using Neural Network

Ship autopilots play an important role in insurance of safe navigation and efficient transportation as else. For their successful design and development, many control techniques were and are being developed. In this paper, the application of artificial neural network (ANN) is investigated to design an autopilot for a surface ship. Feed forward multilayered architecture of ANN is used for approximation of the inverse model of the ship. The designed autopilot acts like an optimal one because of using a cost function for generation of ANN training data. The performance of designed autopilot is evaluated in still water and different wave frequencies. The stability and robustness of the designed system is proved through simulation, carried out in Matlab. The results show that the proposed autopilot can efficiently be used to control the course of a ship in a range of parameter variation. 1. Introduction The main duty of an autopilot is to keep the course of ship in a predefined direction using mostly the rudder. The safety and efficiency of transportation are directly dependent on the performance of ship autopilots. For this, a lot of techniques like proportional-integral-derivative controller, optimal control theory, adaptive control, and nonlinear control are used to improve the autopilot performance. The major feature of these approaches is that they require the exact knowledge of dynamics of controlled objects, which is difficult to obtain in practice due to complexity of ship hydrodynamics. For adaptive and nonlinear control there are some demerits like difficult design and stability analysis. The mentioned above disadvantages can be overcome by using artificial neural network (ANN) because of ANN approximation ability of arbitrary smooth function with required accuracy (Haykin 1999; Terekhov 2002). The first way of application of ANN to ship steering is to use a conventional controller for training the neural network (NN) controller (Endo et al. 1989; Burns & Richter 1996; Unar 1999; Khizer et al. 2013; Pathan et al. 2012a, 2012b; Velagic 2006; Zirilli et al. 2000; Alarcin 2007; Alarcin et al. 2010). Another way to develop NN controller is to integrate NN with other control techniques in sense of adaptive control (Cao et al. 2000; Tzung-hang et al. 2001; Ming-chung & Zi-yi 2013; Li et al. 2004; Jun & Liu 2013; Xu et al. 2014; Xiao et al. 2011; Wang et al. 2013; Yang & Zhao 2006; Leonessa & VanZwieten 2004; Hu & Pan 2012).

Robust Adaptive Fault-Tolerant Control for a Class of Unknown Nonlinear Systems

This paper studies the fault-tolerant tracking control problem for a class of strict-feedback nonlinear systems subjected to actuator faults and external disturbances. The prior knowledge for actuator fault, nonlinearity, and external disturbance is totally unknown, besides the control directions. Based on a backstepping approach, an adaptive fault-tolerant control scheme is developed, without utilizing neural networks. In the control design, a group of new feedback mechanisms are proposed to compensate for the unknown system dynamics and actuator faults. Furthermore, to relax a requirement of the initial system states, a modification technique is designed to adjust the reference signal and virtual control laws for a short time. It is shown that the global closed-loop stability is guaranteed and the tracking performance is achieved. The above result is illustrated via simulations on a one-link manipulator and a ship autopilot.

Adaptive Fuzzy Autopilot for a Scale Ship Model

This paper discusses the implementation of an adaptive fuzzy autopilot for a scale ship model using a Fuzzy Model Reference Learning Controller (FMRLC). Initially, a fuzzy singleton model of the scale ship model is obtained from experimental input and output data gathered from the turning circle manoeuvre. After that, the paper describes how to design a Fuzzy Model Reference Learning Controller (FMRLC) for controlling the directional heading of the scale ship model. At the end, results of the implementation of the adaptive ship autopilot are showed.

The Design of Ship Autopilot by Applying Observer - Based Feedback Linearization

Abstract The paper considers the problem of ship autopilot design based on Bech’s model of the vessel. Since the model is highly nonlinear and some of the state vector coordinates are unavailable, the control system synthesis is performed by means of an output feedback linearization method combined with a nonlinear observer. The asymptotic stability of the overall system has been proven, including the asymptotic stability of the system internal dynamics. The performed simulations of the ship course-changing process have confirmed a high performance of the proposed controller. It has been emphasized that for its practical usability the system robustification is necessary.

A Ship Autopilot Detection and Simulation System

The ship autopilot can't work properly on the land due to the lack of the real rudder angle and heading feedback signal. The paper established the ship motion mathematic model and designed a rudder angle generating device. Applying the real autopilot, a detection and simulation system is developed. The proposed system can connect the output data between the ship autopilot and ship motion mathematic model, then the real ship autopilot performance can be detected and evaluated.

The Development of Marine Autopilot Simulator

The mathematical model of the ship autopilot system is established which centering on marine hydraulic steering gear, based on the Nomoto ship motion model, and considered the influence of wind, wave, flow and other environmental factors on ship course angle. Depend on this model, the physical-digital hybrid simulation autopilot simulator is developed. This simulator has all of the operating functions of the ship steering system, it can not only simulate the turbine engine working scenarios, but also dynamically display the operation process, and the simulator is applicable to the ship track control research and crew training work.

Fuzzy-Approximator-Based Adaptive Controller Design for Ship Course-Keeping Steering in Strict-Feedback Forms

Along with increasing marine transportation and logistics, the ship autopilot has become much important not only to lower the seaman's operating intensions, but also to reduce the seaman's deployment. It is still a challenge to design ship course-keeping controller because of ship's uncertain dynamics and time-varying environmental disturbance. This study focuses on backstepping adaptive course-keeping controller design for ship autopilot. Takagi-Sugeno (T-S) fuzzy approximator can formulate ship motion's uncertainties. Therefore, the proposed controller has no need of a priori knowledge about ship's system dynamics. Command filter can bypass the iterative differential manipulations in conventional ship course adaptive backstepping controller. The design can guarantee the ultimate uniform boundedness of the signals in closed-loop system. Finally, simulation study verifies the efficiency of the ship course-keeping design.

Robust Adaptive Nn Control of Ship Course Autopilot Based on DSC

The problem of adaptive control design of ship autopilot with rudder dynamics is studied. The model is described by a third order nonlinear model for parametric uncertainties. An adaptive neural network (NN ) control algorithm based on dynamic surface control (DSC) is developed. With only one learning parameter and reduced computation load, the proposed algorithm can avoid both problem of explosion of complexity in the conventional backstepping method and singularity problem. In addition, the boundedness stability of the closed-loop system is guaranteed and tracking error can be made arbitrary small. The effectiveness of the presented autopilot has been demonstrated in the simulation.

On the ship course-keeping control system design by using robust feedback linearization

Abstract In the paper the problem of ship autopilot design based on feedback linearization method combined with the robust control approach, is considered. At first the nonlinear ship model (of Norrbin type) is linearized with the use of the simple system nonlinearity cancellation. Next, bearing in mind that exact values of the model parameters are not known, the ensuing inaccuracies are taken as disturbances acting on the system. Thereby is obtained a linear system with an extra term representing the uncertainty which can be treated by using robust, H∞ optimal control techniques. The performed simulations of ship course-changing process confirmed a high performance of the proposed controller despite the assumed significant errors of its parameters.

A nonlilear controller for ship autopilots

Conventional ship autopilots are designed based on a linear ship model using pole - placement technique or linear optimal theory. However, in operation, the ship kinematical parameters can go out the linear limits. In this paper, a nonlinear optimal control law based on aggregated variables is presented. The criterion is chosen so that the dynamic characteristics of object are included. The stability of the closed-loop system is global according to the Lyapunov stability theory. The control law depends explicitly on ship model parameters, so that it is can be easily to tune when the parameters change.

Shaping the Ship Yaw Response by the State-Space Feedback Gain

Ship autopilots are usually designed based on PID controller because of the simplicity and the ease of construct. However its performance in various environmental conditions is not as good as desired. This disadvantage can be decreased by designing a linear state space feedback controller. This paper presents the utility of the state-space feedback controller to stabilize the system and shaping its response as desired. The simulation results for a 4DOF ship with real parameters show the effectiveness of the feedback controller in comparison with ordinary PID ship autopilots.

Ship motion control using multi-controller structure

The aim of this paper is to design a ship autopilot using a multi-controller structure by introducing several working points. The control design of each local controller is achieved using internal model control structure. An illustrative example shows the application of the proposed control structure to a container ship in real condition of navigation.