Autopilot Design for Unmanned Surface Vehicle based on CNN and ACO

  • Dongming Zhao Wuhan University of Technology
  • Tiantian Yang Wuhan University of Technology
  • Wen Ou Huazhong University of Science and Technology
  • Hao Zhou Wuhan University of Technology

Abstract

There is a growing concern to design intelligent controllers for autopiloting unmanned surface vehicles as solution for many naval and civilian requirements. Traditional autopilot’s performance declines due to the uncertainties in hydrodynamics as a result of harsh sailing conditions and sea states. This paper reports the design of a novel nonlinear model predictive controller (NMPC) based on convolutional neural network (CNN) and ant colony optimizer (ACO) which is superior to a linear proportional integral-derivative counterpart. This combination helps the control system to deal with model uncertainties with robustness. The results of simulation and experiment demonstrate the proposed method is more efficient and more capable to guide the vehicle through LOS waypoints particularly in the presence of large disturbances.

References

[1] Abdolmalaki, R.Y.; Mahjoob, M.J.; Abbasi,E. (2013); Fuzzy LQR Controller for Heading Control of an Unmanned Surface Vessel, International Workshops in Electrical-Electronics Engineering, 2013.

[2] Annamalai, Andy S.K. (2014); An adaptive autopilot design for an uninhabited surface vehicle, PhD thesis, University of Plymouth, 2014.

[3] Brando, V.; Lovell, J.; King, E.; Boadle, D.; Scott, R.; Schroeder, T. (2016); The potential of autonomous ship-borne hyperspectral radiometers for the validation of ocean color radiometry data, Remote Sensing, 8(2), 150, 2016.
https://doi.org/10.3390/rs8020150

[4] Dou, C.X. (2003); Design of Fuzzy Neural Network Controller Based on Chaos Neural Network Forecast Model and Application, Systems Engineering-theory & Practice, 23(8), 48–52, 2003.

[5] Dzitac, I. (2015); Impact of Membrane Computing and P Systems in ISI WoS. Celebrating the 65th Birthday of Gheorghe Paun, International Journal of Computers Communications & Control, 10(5), 617–626, 2015.
https://doi.org/10.15837/ijccc.2015.5.2024

[6] Fan, S.Y. (1988); Ship maneuverability, National Defense Industry Press, 1988.

[7] Fitzpatrick, P.J.; Lau, Y.;Moorhead, R.; Skarke, A.; Merritt, D.; Kreider, K.; Brown, C.; Carlon, R.; Hine, G.; Lampoudi, T.; Leonardi, A.P. (2007); A review of the 2014 Gulf of Mexico Wave Glider field program, Marine Technology Society Journal, 49(3), 64–71, 2007.

[8] Gao, F. (2012); Design and Research of Key Technologies for a New AUV in Complex Sea Conditions, PhD thesis, National University of Defense Technology, 2012.

[9] Jiang, L.; Mu, D.; Fan, Y.; Wang, G.; Zhao, Y. (2016); Study on USV model Identification and nonlinear course control, Computer Measurement & Control, 24, 133–136, 2016.

[10] Larrazabal, J.M.; Penas, M.S. (2016); Intelligent rudder control of an unmanned surface vessel, Expert Systems with Applications, 55, 106–117, 2016.
https://doi.org/10.1016/j.eswa.2016.01.057

[11] Li, C.; Zhao, Y.; Wang, G. (2016); Adaptive RBF neural network control for USVcourse tracking, International Conference on Information Science & Technology, 285–290, 2016.

[12] Li, R. (2012); Research and application on Generalized predictive control based on particle swarm optimization algorithm, MSc thesis, LanZhou JiaoTong University, 2012.

[13] Liu, C.; Chu, X.; Wu, Q.; Wang, G. (2014); USV development status and prospects, China Shipbuilding, 194–205, 2014.

[14] Mcninch, L.C.; Muske, K.R.; Ashrafiuon, H. (2008); Model-based predictive control of an unmanned surface vessel, IASTED International Conference on Intelligent Systems and Control, 385–390, 2008.

[15] Mcninch, L.C.; Ashrafiuon, H. (2011); Predictive and sliding mode cascade control for Unmanned Surface Vessels, American Control Conference, 145(2), 184–189, 2011.

[16] Moe, S.; Pettersen, K.Y. (2016); Set-based Line-of-Sight (LOS) path following with collision avoidance for underactuated unmanned surface vessel, In 24th Mediterranean Conference Control and Automation, 402–409, 2016.

[17] Mu, D.; Zhao, Y.; Wang, G.; Fan, Y.; Bai, Y. (2016); USV model identification and course control, Sixth International Conference on Information Science and Technology, 263–267, 2016.

[18] Mu, D.; Zhao, Y.; Wang, G.; Fan, Y.; Bai, Y. (2016); Course control of USV based on fuzzy adaptive guide control, Control and Decision Conference, 6433–6437, 2016.

[19] Ni, H. (2006); Research on Predictive Control Method Based on Neural Network and Its Application, MSc thesis, Central South University, 2006.

[20] Pan, L.; He, C.; Tian, Y.; Su, Y.; Zhang, X. (2017); A region division based diversity maintaining approach for many-objective optimization, Integrated Computer-Aided Engineering, 24(3), 1–18, 2017.

[21] Pan, L.; Paun, G. (2009); Spiking neural P systems with anti-spikes, International Journal of Computers Communications & Control, 4(3), 273–282, 2009.
https://doi.org/10.15837/ijccc.2009.3.2435

[22] Paun, G. (2016); Membrane Computing and Economics: A General View, International Journal of Computers Communications & Control, 11(1), 105-112, 2016.
https://doi.org/10.15837/ijccc.2016.1.2160

[23] Shen, Y.; Cheng, Y.; Ji, Z. (2006); Controller Design for Asynchronism Motor Based on Multi-step Predictive Neural Network, Small & Special Electrical Machines, 34(12), 34–36, 2006.

[24] Sonnenburg, C.; Woolsey, C.A. (2012); An experimental comparison of two USV trajectory tracking control laws, Oceans, 1–10, 2012.

[25] Wang, C.S.; Xiao, H.R.; Han, Y.Z. (2013); Applications of ADRC in Unmanned Surface Vessel Course Tracking, Applied Mechanics & Materials, 427–429:897–900, 2013.

[26] Wang, Y.D. (2014); Based on auto disturbance rejection control algorithm for course autopilot of unmanned surface vessel design, MSc thesis, Dalian Maritime University, 2014.

[27] Wu, G.; Jin, Z.; Lei, W.; Qin, Z. (2009). Design of the Intelligence Motion Control System for the High-Speed USV, Intelligent Computation Technology and Automation, 3, 50–53, 2009.

[28] Yang, J.F. (2007); Ant colony algorithm and its application research, PhD thesis, Zhejiang University, 2007.

[29] Yang, L. (2013); Analysis and Design of Simplified Dual Neural Network Based Model Predictive Controller, MSc thesis, Shanghai Jiao Tong University, 2013.
Published
2018-05-27
How to Cite
ZHAO, Dongming et al. Autopilot Design for Unmanned Surface Vehicle based on CNN and ACO. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 13, n. 3, p. 429-439, may 2018. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/3236>. Date accessed: 11 july 2020. doi: https://doi.org/10.15837/ijccc.2018.3.3236.

Keywords

USV, autopilot, predictive control, Convolution Neural Network (CNN), Ant Colony Optimization (ACO), rolling optimization