Optimized support vector regression algorithm-based modeling of ship dynamics

Abstract This study contributes to solving the problem of how to derive a simplistic model feasible for describing dynamics of different types of ships for maneuvering simulation employed to study maritime traffic and furthermore to provide ship models for simulation-based engineering test-beds. The problem is first addressed with the modification and simplification of a complicated and nonlinearly coupling vectorial representation in 6 degrees of freedom (DOF) to a 3 DOF model in a simple form for simultaneously capturing surge motions and steering motions based on several pieces of reasonable assumptions. The created simple dynamic model is aiming to be useful for different types of ships only with minor modifications on the experiment setup. Another issue concerning the proposed problem is the estimation of parameters in the model through a suitable technique, which is investigated by using the system identification in combination with full-scale ship trail tests, e.g., standard zigzag maneuvers. To improve the global optimization ability of support vector regression algorithm (SVR) based identification method, the artificial bee colony algorithm (ABC) presenting superior optimization performance with the advantage of few control parameters is used to optimize and assign the particular settings for structural parameters of SVR. Afterward, the simulation study on identifying a simplified dynamic model for a large container ship verifies the effectiveness of the optimized identification method at the same time inspires special considerations on further simplification of the initially simplified dynamic model. Finally, the further simplified dynamic model is validated through not only the simulation study on a container ship but also the experimental study on an unmanned surface vessel so-called I-Nav-II vessel. Either simulation study results or experimental study results demonstrate a valid model in a simple form for describing the dynamics of different types’ ships and also validate the performance of the proposed parameter estimation method.

[1]  Man Zhu,et al.  Global Path Planning of Maritime Unmanned Vehicle Based on Real-time Environment Information , 2014 .

[2]  J. Suykens,et al.  Chaos control using least squares support vector machines , 1999 .

[3]  Michael Eric Taylor,et al.  System Identification and Control of an Arleigh Burke Class Destroyer Using an Extended Kalman Filter , 2000 .

[4]  Zhixiang Liu,et al.  Unmanned surface vehicles: An overview of developments and challenges , 2016, Annu. Rev. Control..

[5]  Craig A. Woolsey,et al.  Modeling, Identification, and Control of an Unmanned Surface Vehicle , 2013, J. Field Robotics.

[6]  Ksm Davidson,et al.  TURNING AND COURSE KEEPING QUALITIES OF SHIPS , 1946 .

[7]  A. Atiya,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2005, IEEE Transactions on Neural Networks.

[8]  Axel Hahn,et al.  Comparison and optimization of the parameter identification technique for estimating ship response models , 2017, 2017 3rd IEEE International Conference on Control Science and Systems Engineering (ICCSSE).

[9]  Key Pyo Rhee,et al.  Identification of hydrodynamic coefficients in ship maneuvering equations of motion by Estimation-Before-Modeling technique , 2003 .

[10]  J. DiStefano,et al.  Identifiability of Model Parameter , 1985 .

[11]  Roger Skjetne,et al.  A Nonlinear Ship Manoeuvering Model: Identification and adaptive control with experiments for a model ship , 2004 .

[12]  K Kijima,et al.  PREDICTION METHOD OF SHIP MANOEUVRABILITY IN DEEP AND SHALLOW WATERS , 1990 .

[13]  Joaquín Aranda Almansa,et al.  Identification of a Surface Marine Vessel Using LS-SVM , 2013, J. Appl. Math..

[14]  Zhongyi Hu,et al.  Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting , 2014, Knowl. Based Syst..

[15]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[16]  Weilin Luo,et al.  Parameter Identifiability of Ship Manoeuvring Modeling Using System Identification , 2016 .

[17]  Man Zhu,et al.  Identification-based simplified model of large container ships using support vector machines and artificial bee colony algorithm , 2017 .

[18]  Feng Xu,et al.  Sensitivity analysis of the hydrodynamic coefficients in 4 degrees of freedom ship manoeuvring mathematical model , 2015 .

[19]  Yoshitaka Furukawa,et al.  ON THE MANOEUVRING PERFORMANCE OF A SHIP WITH THE PARAMETER OF LOADING CONDITION , 1990 .

[20]  Zao-jian Zou,et al.  Parameter identification of nonlinear roll motion equation for floating structures in irregular waves , 2016 .

[21]  L W Smitt,et al.  ANALOGUE SIMULATION OF SHIP MANEUVERS BASED ON FULL SCALE TRIALS OR FREE SAILING MODEL TESTS , 1969 .

[22]  Man Zhu,et al.  A Global Path Planning Algorithm of Unmanned Vessel in Inland Waterway , 2013 .

[23]  Karl Johan Åström,et al.  Identification of ship steering dynamics , 1976, Autom..

[24]  Laura Diosan,et al.  Improving classification performance of Support Vector Machine by genetically optimising kernel shape and hyper-parameters , 2010, Applied Intelligence.

[25]  Kensaku Nomoto,et al.  On the Coupled Motion of Steering and Rolling of a High Speed Container Ship , 1981 .

[26]  Man Zhu,et al.  Design and Analysis of Collaborative Unmanned Surface-Aerial Vehicle Cruise Systems , 2019 .

[27]  J. N. Newman The motions of a floating slender torus , 1977, Journal of Fluid Mechanics.

[28]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[29]  Hung Duc Nguyen Recursive identification of ship manoeuvring dynamics and hydrodynamics , 2008 .

[30]  Man Zhu,et al.  Simulative evaluation of applying optimized support vector machines to identify the simplified ship dynamic model , 2017, 2017 IEEE 15th International Conference on Industrial Informatics (INDIN).

[31]  C. Guedes Soares,et al.  Parameter Identification of Ship Maneuvering Model Based on Support Vector Machines and Particle Swarm Optimization , 2016 .

[32]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[33]  J. Takashina,et al.  A practical calculation method of ship maneuvering motion , 1981 .

[34]  Thor I. Fossen,et al.  Fuel-efficient rudder and propeller control allocation for marine craft: experiments with a model ship , 2003, IEEE Trans. Control. Syst. Technol..

[35]  Li Fu,et al.  The Research Survey of System Identification Method , 2013, 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics.

[36]  P. Krishnankutty,et al.  Sensitivity Study of Hydrodynamic Derivative Variations on the Maneuverability Prediction of a Container Ship , 2015 .

[37]  K K Fedyaevsky,et al.  CONTROL AND STABILITY IN SHIP DESIGN , 1964 .

[38]  Masayoshi Hirano 7. A Practical Calculation Method of Ship Maneuvering Motion at Initial Design Stage , 1981 .

[39]  Kensaku Nomoto,et al.  On the steering qualities of ships , 1956 .

[40]  S. L. Toxopeus Practical application of viscous-flow calculations for the simulation of manoeuvring ships , 2011 .

[41]  Key Pyo Rhee,et al.  Sensitivity analysis of submersibles’ manoeuvrability and its application to the design of actuator inputs , 2006 .

[42]  M A Abkowitz,et al.  LECTURES ON SHIP HYDRODYNAMICS--STEERING AND MANOEUVRABILITY , 1964 .

[43]  Rob J Hyndman,et al.  Another look at measures of forecast accuracy , 2006 .

[44]  Bernhard Schölkopf,et al.  Extracting Support Data for a Given Task , 1995, KDD.

[45]  Xu Feng,et al.  Parametric Identification of Abkowitz Model for Ship Maneuvering Motion by Using Partial Least Squares Regression , 2015 .

[46]  W. L. Luo,et al.  Parametric Identification of Ship Maneuvering Models by Using Support Vector Machines , 2009 .

[47]  Odd M. Faltinsen,et al.  Hydrodynamics of High-Speed Marine Vehicles , 2006 .

[48]  N H Norrbin,et al.  THEORY AND OBSERVATIONS ON THE USE OF A MATHEMATICAL MODEL FOR SHIP MANOEUVRING IN DEEP AND CONFINED WATERS , 1971 .

[49]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[50]  Man Zhu,et al.  Comprehensive Evaluation Cloud Model for Ship Navigation Adaptability , 2014 .

[51]  Tristan Perez,et al.  TIME-DOMAIN MODELS OF MARINE SURFACE VESSELS BASED ON SEAKEEPING COMPUTATIONS , 2006 .

[52]  Wei-yuan Hwang Application of system identification to ship maneuvering , 1980 .

[53]  Zaojian Zou,et al.  Estimation of the hydrodynamic coefficients from captive model test results by using support vector machines , 2013 .

[54]  Cecilia Laschi,et al.  A systematic method for dynamic modeling and identification of a small-sized autonomous surface vehicle using simulated annealing techniques , 2013, 2013 MTS/IEEE OCEANS - Bergen.

[55]  Alexander J. Smola,et al.  Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.

[56]  N H Norrbin ON THE DESIGN AND ANALYSIS OF THE ZIG-ZAG TEST ON BASE OF QUASI-LINEAR FREQUENCY RESPONSE , 1963 .

[57]  M. Caccia,et al.  A Practical Approach to Modeling and Identification of Small Autonomous Surface Craft , 2008, IEEE Journal of Oceanic Engineering.

[58]  HU Shi-ju Calculating Traffic Capacity of Water-network Channel Based on Cut Set Method , 2014 .

[59]  Leigh McCue,et al.  Handbook of Marine Craft Hydrodynamics and Motion Control [Bookshelf] , 2016, IEEE Control Systems.

[60]  S. K. Bhattacharyya,et al.  Parametric Identification for Nonlinear Ship Maneuvering , 2006 .

[61]  Edward V. Lewis,et al.  Principles of naval architecture , 1988 .

[62]  C. Guedes Soares,et al.  Real-Time Parameter Estimation of Nonlinear Vessel Steering Model Using Support Vector Machine , 2018 .

[63]  Weilin Luo,et al.  Control for Ship Course-Keeping Using Optimized Support Vector Machines , 2016, Algorithms.

[64]  Shosuke Inoue,et al.  Hydrodynamic derivatives on ship manoeuvring , 1981 .

[65]  Hu Qing-bo Comprehensive Evaluation of LNG Ship's Traffic Adaptability Based on Cloud Model , 2013 .

[66]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[67]  Elías Revestido Herrero,et al.  Two-step identification of non-linear manoeuvring models of marine vessels , 2012 .

[68]  Xinyu Li,et al.  Measures to diminish the parameter drift in the modeling of ship manoeuvring using system identification , 2017 .

[69]  Benjamin Racine,et al.  CFD-Based Method for Simulation of Marine-Vehicle Maneuvering , 2005 .

[70]  Naoya Umeda,et al.  Estimating maneuvering coefficients using system identification methods with experimental, system-based, and CFD free-running trial data , 2012 .

[71]  J. P Hooft The cross-flow drag on a manoeuvring ship , 1994 .

[72]  Christian Denker,et al.  Assessing the Spatio-Temporal Fitness of Information Supply and Demand on an Adaptive Ship Bridge , 2014, EKAW.

[73]  Hussain Shareef,et al.  An application of artificial bee colony algorithm with least squares support vector machine for real and reactive power tracing in deregulated power system , 2012 .

[74]  Zhu Ma Comprehensive Evaluation of Shipwreck's Navigation Obstruction Based on Cloud Model , 2014 .

[75]  Mogens Blanke,et al.  Mathematical Ship Modeling for Control Applications by Tristan Pérez † and , 2002 .

[76]  M. Blanke,et al.  A Linearized State-space Model of Steering and Roll of a High-speed Container Ship , 1986 .

[77]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[78]  Alexandre N. Simos,et al.  Hydrodynamic Model Induced Differences in SPM Post Pitchfork Bifurcation Paths , 2001 .

[79]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[80]  Axel Hahn,et al.  Virtual test bed for maritime safety assessment , 2015 .

[81]  Stephen J. Pullard Recursive Parameter Identification for Estimating and Displaying Maneuvering Vessel Path , 2003 .

[82]  Man Zhu,et al.  Maritime Unmanned Vehicle Cruise Path Planning for Maritime Information Collection , 2016 .

[83]  Alex Smola,et al.  Kernel methods in machine learning , 2007, math/0701907.

[84]  Man Zhu,et al.  Identification-based controller design using cloud model for course-keeping of ships in waves , 2018, Eng. Appl. Artif. Intell..

[85]  Axel Hahn,et al.  Test Bed for Safety Assessment of New e-Navigation Systems , 2014 .

[86]  Karl Johan Åström,et al.  Design of Fixed Gain and Adaptive Ship Steering Autopilots Based on the Nomoto Model , 1980 .

[87]  S. K. Bhattacharyya,et al.  System identification for nonlinear maneuvering of large tankers using artificial neural network , 2008 .

[88]  Man Zhu,et al.  Research Of Unmanned Surface Vessel (USV) Path-Planning Algorithm Based on ArcGIS , 2013 .

[89]  Zou Zao-jian,et al.  Parametric estimation of ship maneuvering motion with integral sample structure for identification , 2015 .

[90]  Andre Bolles,et al.  Parameter Identification of Ship Maneuvering Models Using Recursive Least Square Method Based on Support Vector Machines , 2017 .

[91]  Thor I. Fossen,et al.  Guidance and control of ocean vehicles , 1994 .

[92]  C. Soares,et al.  An algorithm for offline identification of ship manoeuvring mathematical models from free-running tests , 2014 .

[93]  A. Zell,et al.  Efficient parameter selection for support vector machines in classification and regression via model-based global optimization , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..