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Course keeping Control Based on Integrated Nonlinear Feedback for a USV with Pod-like Propulsion

Published online by Cambridge University Press:  13 February 2018

Yunsheng Fan ,
Dongdong Mu ,
Xianku Zhang ,
Guofeng Wang  and
Chen Guo
Yunsheng Fan*
Affiliation:
(Information Science and Technology College, Dalian Maritime University, Dalian, China)
Dongdong Mu
Affiliation:
(Information Science and Technology College, Dalian Maritime University, Dalian, China)
Xianku Zhang
Affiliation:
(Navigation College, Dalian Maritime University, Dalian, China)
Guofeng Wang
Affiliation:
(Information Science and Technology College, Dalian Maritime University, Dalian, China)
Chen Guo
Affiliation:
(Information Science and Technology College, Dalian Maritime University, Dalian, China)
*
(E-mail: [email protected])
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Abstract

In this paper, a response model of an Unmanned Surface Vehicle (USV) with a pod-like propulsion device is established. To improve the robustness of motion control in heavy sea states, an integrated nonlinear feedback course-keeping controller is proposed. First, to establish a response model of a USV with pod-like propulsion, model parameters are obtained by the method of system identification, then an integrated nonlinear feedback control strategy is proposed. The essence of this method is to make the original error signal pass through a nonlinear function, and then the output of this function is used to replace the original error signal. Simulation results show that under ordinary sea states, nonlinear feedback can save up to 34.5% of energy used compared with standard feedback methods; under heavy sea states, this can rise to 40.8%. A set of field experiments were carried out with a USV with pod-like propulsion. Results show that under heavy sea states, the test USV can maintain the target course well, which proves the correctness of the model and the robustness of the proposed method.

Keywords

USVPod-like propulsionModel Parameters IdentificationCourse Keeping ControlIntegrated Nonlinear Feedback
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 4 , July 2018 , pp. 878 - 898
Copyright
Copyright © The Royal Institute of Navigation 2018 

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References

REFERENCES

Abkowitz, M A. (1964). Lectures on ship hydrodynamics–Steering and manoeuvrability. Report Hy-5, Hydro-and Aerodynamic Laboratory, Denmark.Google Scholar
Du, J., Guo, C.and Yang, C. (2006). Adaptive tracking controller design for ship course nonlinear system. Journal of Applied Science, 24(1), 83–88.Google Scholar
Fan, Y., Sun, X., Wang, G.and Guo, C. (2015). On fuzzy self-adaptive PID control for USV course. 34th China Control Conference (CCC), Hangzhou China, IEEE, 8472–8478.Google Scholar
Fan, Y., Ge, Z., Zhao, Y.and Wang, G. (2015) Design of information network and control system for USV. 54th IEEE Annual Conference of the Society of Instrument and Control Engineers of Japan, 1126–1131.Google Scholar
Fang, M.C. and Luo, J.H. (2007). On the track keeping and roll reduction of the ship in random waves using different sliding mode controllers. Ocean Engineering, 34 (3/4), 479–488.Google Scholar
Gao, J. and Chen, G.Y. (2010). Fuzzy sliding mode control and simulation for ship's course steering. Journal of Jiangsu University of Science and Technology: Natural Science Edition, 24(4), 372–376.Google Scholar
Jia, X. and Yang, Y. (1999). The mathematical model of ship motion mechanism modeling and identification modeling. Dalian Maritime University Press, 234–238.Google Scholar
Li, J., Li, T.and Li, Y. (2012). NN-based adaptive dynamic surface control for a class of nonlinear systems with input saturation. 7th Industrial Electronics and Applications (ICIEA), Singapore, IEEE, 570–575.Google Scholar
Li, Y. and Tong, S. (2016). Adaptive neural networks decentralized FTC design for nonstrict-feedback nonlinear interconnected large-scale systems against actuator faults, IEEE Transactions on Neural Networks and Learning Systems, 28(11), 2541–2554.Google Scholar
Li, Y., Tong, S., Liu, L.and Feng, G. (2017). Adaptive output-feedback control design with prescribed performance for switched nonlinear systems. Automatica, 225–231.Google Scholar
Li, R., Li, T., Bu, R, Zheng, Q and Chen, P. (2013). Active Disturbance Rejection with Sliding Mode Control Based Course and Path Following for Underactuated Ships. Mathematical Problems in Engineering, (1), 1–9.Google Scholar
Mu, D., Wang, G., Fan, Y.and Zhao, Y. (2017). Modeling and Identification of Podded Propulsion Unmanned Surface Vehicle and Its Course Control Research. Mathematical Problems in Engineering, (2), 1–13.Google Scholar
Nomoto, K., Taguchi, K., Honda, K.and Hirano, S. (2009). On the Steering Qualities of Ships. International Shipbuilding Progress, 4(35), 354–370.Google Scholar
Norrbin, N.H. (1971). Theory and Observations on the Use of a Mathematical Model for Ship Manoeuvring in Deep and Confined Waters. SSPA Report No.68.Google Scholar
Peng, Z., Wang, D., Wang, W.and Liu, L. (2016). Neural adaptive steering of an unmanned surface vehicle with measurement noises. Neurocomputing, 186(C), 228–234.Google Scholar
Sakuma, S. and Naruse, T. (2016). On the Steering Quality Indices of Some POD. Driven Ships, 23, 27–32.Google Scholar
Sarda, E.I., Bertaska, I.R., Qu, A.and Ellenrieder, K.D.V. (2015). Development of a USV station-keeping controller. Oceans, Genoa, Italy, IEEE, 1–10.Google Scholar
Skjetne, R., Smogeli, Ø.and Fossen, T.I. (2004). Modeling, Identification, and Adaptive Maneuvering of CyberShip II: A complete design with experiments. IFAC Proceedings Volumes, 37(10), 203–208.CrossRefGoogle Scholar
Sonnenburg, C.R. and Woolsey, C.A. (2013). Modeling, identification, and control of an unmanned surface vehicle. Journal of Field Robotics, 30(3), 371–398.Google Scholar
Tong, S., Huo, B.and Li, Y. (2014). Observer-Based Adaptive Decentralized Fuzzy Fault-Tolerant Control of Nonlinear Large-Scale Systems With Actuator Failures. IEEE Transactions on Fuzzy Systems, 22(1), 1–15.Google Scholar
Wang, D. and Huang, J. (2005). Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form. IEEE Transactions on Neural Networks, 16(1), 195–202.Google Scholar
Yuan, L. and Wu, H.S. (2010). Terminal sliding mode fuzzy control based on multiple sliding surfaces for nonlinear ship autopilot systems. Journal of Ship and Ocean Engineering: English Edition, 9(4), 425–430.Google Scholar
Yang, C., Jia, X.and Bi, Y. (2001). Ship rudder roll stabilization and robust control. Dalian Maritime University Press.Google Scholar
Zhang, X.K. and Jia, X.L. (2002). Simplification of H∞ mixed sensitivity algorithm and its application. Automatic Control and Computer Sciences, 36(3), 28–33.Google Scholar
Zhang, X.K., Yang, G., Zhang, Q.and Zhang, Y. (2017). Improved Concise Backstepping Control of Course Keeping for Ships Using Nonlinear Feedback Technique. Journal of Navigation, 70(6), 1401–1414.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.Google Scholar
Zhang, X. (2011). Control Algorithm for Autopilot Driven by Sine of Course Deviation. Navigation of China, 34(1), 1–4.Google Scholar