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An improved nonlinear innovation-based parameter identification algorithm for ship models

Published online by Cambridge University Press:  05 March 2021

Baigang Zhao Open the ORCID record for Baigang Zhao [Opens in a new window]  and
Xianku Zhang
Baigang Zhao
Affiliation:
Navigation College, Dalian Maritime University, Dalian, China
Xianku Zhang*
Affiliation:
Navigation College, Dalian Maritime University, Dalian, China
*
*Corresponding author. E-mail: [email protected]
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Abstract

To solve the problem of identifying ship model parameters quickly and accurately with the least test data, this paper proposes a nonlinear innovation parameter identification algorithm for ship models. This is based on a nonlinear arc tangent function that can process innovations on the basis of an original stochastic gradient algorithm. A simulation was carried out on the ship Yu Peng using 26 sets of test data to compare the parameter identification capability of a least square algorithm, the original stochastic gradient algorithm and the improved stochastic gradient algorithm. The results indicate that the improved algorithm enhances the accuracy of the parameter identification by about 12% when compared with the least squares algorithm. The effectiveness of the algorithm was further verified by a simulation of the ship Yu Kun. The results confirm the algorithm's capacity to rapidly produce highly accurate parameter identification on the basis of relatively small datasets. The approach can be extended to other parameter identification systems where only a small amount of test data is available.

Keywords

ship modelparameter identificationnonlinear innovationstochastic gradient
Type
Research Article
Information
The Journal of Navigation , Volume 74 , Issue 3 , May 2021 , pp. 549 - 557
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Copyright
Copyright © The Royal Institute of Navigation 2021

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References

Astrom, K. and Källström, C. (1976). Identification of ship steering dynamics. Automatica, 12(1), 9–22.CrossRefGoogle Scholar
Bai, W., Ren, J. and Li, T. (2018). Multi-innovation gradient iterative locally weighted learning identification for a nonlinear ship maneuvering system. China Ocean Engineering, 32(3), 288–300.CrossRefGoogle Scholar
Bai, W., Ren, J. and Li, T. (2019). Modified genetic optimization-based locally weighted learning identification modeling of ship maneuvering with full scale trial. Future Generation Computer Systems, 93, 1036–1045.CrossRefGoogle Scholar
Cheng, W. and Li, K. (2019). Aitken-based stochastic gradient algorithm for ARX models with time delay. Circuits Systems and Signal Processing, 38, 2863–2876.Google Scholar
Deng, Y. and Zhang, X. (2020). Event-triggered composite adaptive fuzzy output feedback control for path following of autonomous surface vessels. IEEE Transactions on Fuzzy Systems, 3006562.CrossRefGoogle Scholar
Ding, F. (2013). New Theory of System Identification. Beijing: Science Press.Google Scholar
Ding, F. and Yang, J. (1999). Convergence analysis of stochastic gradient algorithm. Journal of Tsinghua University. (Science & Technology), 39(1), 83–86.Google Scholar
Jia, X. and Zhang, X. (2001). Intelligent Control and ${{\rm H}_\infty }$ Robust Control of Ship Motion. Dalian: Dalian Maritime University Press.Google Scholar
Nagumo, J. and Noda, A. (1967). A learning method for system identification. IEEE Transactions on Automatic Control, 12(3), 282–287.CrossRefGoogle Scholar
Nomoto, K., Taguchi, T., Honda, K. and Hirnao, S. (1957). On the steering qualities of ships. International Shipbuilding Progress, 4(35), 354–370.CrossRefGoogle Scholar
Qin, Y., Ma, Y., Zhang, L. and Li, T. (2016). Parameter identification of ship's maneuvering motion based on improved least squares method. Journal of Jilin University (Engineering and Technology Edition), 46(3), 897–901.Google Scholar
Sutulo, S. and Soares, C. (2014). An algorithm for offline identification of ship manoeuvring mathematical models from free-running tests. Ocean Engineering, 79, 10–25.CrossRefGoogle Scholar
Wang, C. (2012). Identification of Mathematic Model of the Model Ship. M.D. thesis, Dalian Maritime University, Dalian.Google Scholar
Xie, S., Chu, X., Liu, C. and Wu, Q. (2017). Parameter identification of ship maneuvering response model based on multi-innovation least squares algorithm. Navigation of China, 40(1), 73–78.Google Scholar
Zhang, G., Chu, S., Jin, X. and Zhang, W. (2020a). Composite neural learning fault-tolerant control for underactuated vehicles with event-triggered input. IEEE Transactions on Cybernetics. doi:10.1109/TCYB.3005800.CrossRefGoogle Scholar
Zhang, G., Yao, M., Xu, J. and Zhang, W. (2020b). Robust neural event-triggered control for dynamic positioning ships with actuator faults. Ocean Engineering, 207, 107292.CrossRefGoogle Scholar
Zhang, G., Zhang, C., Li, J. and Zhang, X. (2020c). Improved composite learning path-following control for the underactuated cable-laying ship via the double layers logical guidance. Ocean Engineering, 207, 107342.CrossRefGoogle Scholar
Zhang, G., Zhang, X. and Pang, H. (2015). Multi-innovation auto-constructed least squares identification for 4 DOF ship manoeuvring modelling with full-scale trial data. ISA Transactions, 58, 186–195.CrossRefGoogle ScholarPubMed
Zhang, Q. and Zhang, X. (2019). Nonlinear improved concise backstepping control of course keeping for ships. IEEE Access, 7(6), 19258–19265.CrossRefGoogle Scholar
Zhang, X. (2012). Concise Robust Control for Marine Ships. Beijing: Science Press.Google Scholar
Zhang, X. and Jin, Y. (2013). Control System Modeling and Digital Simulation. Dalian: Dalian Maritime University Press.Google Scholar
Zhang, X. and Zhang, G. (2016). Design of ship course-keeping autopilot using a sine function based nonlinear feedback technique. Journal of Navigation, 69(2), 246–256.CrossRefGoogle Scholar
Zhang, X., Zhang, Q. and Ren, H. (2018). Linear reduction of backstepping algorithm based on nonlinear decoration for ship course-keeping control system. Ocean Engineering, 147, 1–8.CrossRefGoogle Scholar
Zhang, Y. (2017). Application of stochastic gradient genetic algorithm in target recognition of ship wake. Ship Science and Technology, 39(6), 25–27.Google Scholar
Zhou, W. and Blanke, M. (1989). Identification of a class of nonlinear state-space models using RPE techniques. IEEE Transactions on Automatic Control, 34(3), 312–316.CrossRefGoogle Scholar
Zhu, M., Hahn, A. and Wen, Y. (2017). Identification-based simplified model of large container ships using support vector machines and artificial bee colony algorithm. Applied Ocean Research, 68(10), 249–261.CrossRefGoogle Scholar