Unmanned wave glider heading model identification and control by artificial fish swarm algorithm

Abstract

We introduce the artificial fish swarm algorithm for heading motion model identification and control parameter optimization problems for the “Ocean Rambler” unmanned wave glider (UWG). First, under certain assumptions, the rigid-flexible multi-body system of the UWG was simplified as a rigid system composed of “thruster + float body”, based on which a planar motion model of the UWG was established. Second, we obtained the model parameters using an empirical method combined with parameter identification, which means that some parameters were estimated by the empirical method. In view of the specificity and importance of the heading control, heading model parameters were identified through the artificial fish swarm algorithm based on tank test data, so that we could take full advantage of the limited trial data to factually describe the dynamic characteristics of the system. Based on the established heading motion model, parameters of the heading S-surface controller were optimized using the artificial fish swarm algorithm. Heading motion comparison and maritime control experiments of the “Ocean Rambler” UWG were completed. Tank test results show high precision of heading motion prediction including heading angle and yawing angular velocity. The UWG shows good control performance in tank tests and sea trials. The efficiency of the proposed method is verified.

Keywords

unmanned wave glider / artificial fish swarm algorithm / heading model / parameters identification / control parameters optimization

References

[1] WangX-m, ShangJ-z, LuoZ-r, TangL, ZhangX-p, LiJuan. Reviews of power systems and environmental energy conversion for unmanned underwater vehicles [J]. Renewable and Sustainable Energy Reviews, 2012, 16(4): 1958-1970

[2] HuY-l, WangJ-jun. Study on power generation and energy storage system of a solar powered autonomous underwater vehicle (SAUV) [J]. Energy Rocedia, 2012, 16: 2049-2053

[3] MaZ-s, LiuY-h, WangY-h, WangS-xin. Improvement of working pattern for thermal underwater glider [C]. Oceans2016, 2016, Shanghai, China, IEEE: 16

[4] HookD, DaltryR, CoelhoR. C-enduro USV developed for offshore long-endurance applications [J]. Sea Technology, 2014, 55(3): 15

[5] PlumetF, PetresC, Romero-RamirezM A, GasB, LengS H. Toward an autonomous sailing boat [J]. IEEE Journal of Oceanic Engineering, 2015, 402397-407

[6] LiaoY-l, LiY, LiuT, LiY-m, WangL-f, JiangQ-quan. Unmanned wave glider technology: State of the art and perspective [J]. Journal of Harbin Engineering University, 2016, 37(9): 1227-1236

[7] BensonM A, LecoqT, MercadoG, NakayamaS, MoldoveanuN, CaprioliP, NyeinG, PaiS, YandonE. Acquisition using autonomous marine vehicles: Wave glider field test, offshore Abu Dhabi [C]. SPE Middle East Oil & Gas Show and Conference. Manama, Bahrain: Society of Petroleum Engineering, 201769

[8] LiaoY-l, WangL-f, LiY, LiY-m, JiangQ-quan. The intelligent control system for an unmanned wave glider [J]. PlosOne, 2016, 11(12): e0168792

[9] HermsdorferK, WangQ L R, YamaguchiR T, KalogirosJ. Autonomous wave gliders for air-sea interaction research [C]. The 19th Conference on Air-Sea Interaction, 201525-31

[10] XuC-y, ChenJ-w, ZhengB-huan. Research status and applications of wave glider [J]. Journal of Ocean Technology, 2014, 33(2): 111-117

[11] GoebelN L, FrolovS, EdwardsC A. Complementary use of wave glider and satellite measurements: Description of spatial decorrelation scales in Chl-a fluorescence across the pacific basin [J]. Methods in Oceanography, 2014, 10: 90-103

[12] van LankerV, BaeyeM. Wave glider monitoring of sediment transport and dredge plumes in a shallow marine sandbank environment [J]. PlosOne, 2015, 10(6): e0128948

[13] JiangC-m, WanL, SunY-shan. Design of motion control system of pipeline detection AUV [J]. Journal of Central South University, 2017, 243637-646

[14] TanP-l, SunQ-l, JiangY-x, ZhuE-l, ChenZ-q, HeY-ping. Trajectory tracking of powered parafoil based on characteristic model based all-coefficient adaptive control [J]. Journal of Central South University, 2017, 24(5): 1073-1081

[15] SmithR N, DasJ, HineG, AndersonW, SukhatmeG S. Predicting wave glider speed from environmental measurements [C]. Oceans2011, 2011, Kona, USA, IEEE/MTS: 18

[16] NgoP, Al-SabbanaW, ThomasJ, AndersonW, DasJ, SmithR N. An analysis of regression models for predicting the speed of a wave glider autonomous surface vehicle [C]. Australasian Conference on Robotics and Automation, 2013, Sydney, Australia, University of New South Wales: 19

[17] NgoP, DasJ, OgleJ, ThomasJ, AndersonW, SmithR N. Predicting the speed of a wave glider autonomous surface vehicle from wave model data [C]. IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS2014), 2014, Chicago, USA, IEEE/RSJ: 22502256

[18] TianB-q, YuJ-c, ZhangA-qun. Dynamic modeling of wave driven unmanned surface vehicle in longitudinal profile based on D-H approach [J]. Journal of Central South University, 2015, 22(12): 4578-4584

[19] LiaoY-l, LiY-m, WangL-f, LiY, JiangQ-quan. Heading control method and experiments for an unmanned wave glider [J]. Journal of Central South University, 2017, 24(11): 2504-2512

[20] KrausN D, BinghamB. Estimation of wave glider dynamics for precise positioning [C]. Oceans2011, 2011, Kona, USA, IEEE/MTS: 19

[21] KrausN DWave glider dynamic modeling, parameter identification and simulation [D], 2012, Hawaii, USA, University of Hawaii

[22] LuXuResearch on the general technology of wave glider [D], 2015, Harbin, China, Harbin Engineering University

[23] LiX-taoDynamic model and simulation study based on the wave glider [D], 2014, Beijing, China, China Ship Research and Development Academy

[24] QiZ-f, LiuW-x, JiaL-y, QinY-f, SunX-jun. Dynamic modeling and motion simulation for wave glider [J]. Applied Mechanics and Materials, 2013, 397–400: 285-290

[25] JiaL-yuanStudy of operation principle of two-part architecture and dynamic behavior of wave glider [D], 2014, Tianjin, China, National Ocean Technology Center

[26] FossenT IHandbook of marine craft hydrodynamics and motion control First Edition [M], 2011, Chichester, John Wiley & Sons Ltd

[27] ZhouZ-m, ShengZ-y, FengW-shi. Maneuverability prediction and calculation of multi-purpose cargo vessels [J]. Ship Engineering, 1983, 6: 21-29

[28] ClarkeD, GedlingP, HineG. The application of manoeuvring criteria in hull design using linear theory [J]. Naval Architect, 1983, 125: 45-68

[29] ShenW, GuoX-p, WuC, WuD-sheng. Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm [J]. Knowledge-Based Systems, 2011, 24(3): 378-385

[30] LiX-leiA new intelligent optimization method-artificial fish swarm algorithm [D], 2003, Hangzhou, China, Zhejiang University

[31] HuG-b, QiuX-s, MengL-ming. Radio frequency identification network planning for indoor localization based on enhanced artificial fish swarm algorithm [J]. Journal of Computational & Theoretical Nanoscience, 2016, 13(11): 8963-8969

[32] BharathiT, KrishnakumariP. Application of modified artificial fish swarm algorithm for optimizing association rule mining [J]. Technology & Indian Journal of Science, 2014, 7(12): 1906-1915

[33] WuH-f, ChenX-q, ShiC-j, XuMing. An ACOA-AFSA fusion routing algorithm for underwater wireless sensor network [J]. International Journal of Distributed Sensor Networks, 2012, 8(5): 1-9

[34] LiaoY-l, WanL, ZhuangJ-yuan. The embedded motion control system for the water-jet-propelled unmanned surface vehicle [J]. Chinese High Technology Letters, 2012, 22(4): 416-422