LIANG Liang1 (梁 良), SHI Ying1∗ (石 英), MOU Junmin2∗ (牟军敏) . Submarine Multi-Model Switching Control Under Full Working Condition Based on Machine Learning[J]. Journal of Shanghai Jiaotong University(Science), 2022 , 27(3) : 402 -410 . DOI: 10.1007/s12204-021-2284-6

[1] BETTLE M C, GERBER A G, W ATT G D. Using reduced hydrodynamic models to accelerate the predictor-corrector convergence of implicit 6-DOF URANS submarine manoeuvring simulations [J]. Computers & Fluids, 2014, 102: 215-236. [2] KIM H, RANMUTHUGALA D, LEONG Z Q, et al. Six-DOF simulations of an underwater vehicle undergoing straight line and steady turning manoeuvres [J]. Ocean Engineering, 2018, 150: 102-112. [3] FERRI G, MUNAF ò A , L E P A G E K D . A n a u tonomous underwater vehicle data-driven control strategy for target tracking [J]. IEEE Journal of Oceanic Engineering, 2018, 43(2): 323-343. [4] ZHANG L Q, SHAO C. An adaptive learning method for the generation of fuzzy inference system from data [J]. Acta Automatica Sinica, 2008, 34(1): 80-85. [5] JAGTAP P, RAUT P, KUMAR P, et al. Control of autonomous underwater vehicle using reduced order model predictive control in three dimensional space [J]. IF AC-PapersOnLine, 2016, 49(1): 772-777. [6] YU C Y, XIANG X B, WILSON P A, et al. Guidanceerror-based robust fuzzy adaptive control for bottom following of a flight-style AUV with saturated actuator dynamics [J]. IEEE Transactions on Cybernetics, 2020, 50(5): 1887-1899. [7] ANTONELLI G, CACCA V ALE F, CHIA VERINI S. Adaptive tracking control of underwater vehiclemanipulator systems based on the virtual decomposition approach [J]. IEEE Transactions on Robotics and Automation, 2004, 20(3): 594-602. [8] V AN DEN BRAEMBUSSCHE P, SWEVERS J, V AN BRUSSEL H. Design and experimental validation of robust controllers for machine tool drives with linear motor [J]. Mechatronics, 2001, 11(5): 545-562. [9] YAO G, TANG T H. A variable structure controller for keeping ship course based on combination of PD and fuzzy controller [J]. Journal of Shanghai Maritime University, 2003, 24(3): 200-204 (in Chinese). [10] ZHOU H Y，LIU Y P，HU Z Q, et al. Switching strategy of dynamic sliding mode control based on multiple identification model set for unmanned semisubmersible vehicle [J]. Acta Armamentarii, 2017, 38(11): 2198-2206 (in Chinese). [11] ZHOU H Y, LI Y P, LIU K Z, et al. Multi-model switching control laws for the vertical plane control of an autonomous underwater vehicle [J]. Journal of Harbin Engineering University, 2017, 38(8): 13091315 (in Chinese). [12] JOHANSEN T A, FOSS B A. Multiple model approaches to modelling and control [J]. International Journal of Control, 1999, 72(7/8): 575. [13] SHI S D. Submarine maneuvering characteristic [M].Beijing: National Defense Industry Press, 1995 (in Chinese). [14] BABAOGLU O K. Designing an automatic control system for a submarine [D]. Monterey: Naval Postgraduate School, 1988. [15] LEI H M, SHAO L, YANG Z. Theory and method for multiple-model modeling and control [M]. Beijing: National Defense Industry Press, 2017 (in Chinese). ? 14 Machine Learning-Based Approach to Liner Shipping Schedule Design [1] VERNIMMEN B, DULLAERT W, ENGELEN S. Schedule unreliability in liner shipping: Origins and consequences for the hinterland supply chain [J]. Maritime Economics & Logistics, 2007, 9: 193-213. [2] NOTTEBOOM T E. The time factor in liner shipping services [J]. Maritime Economics & Logistics, 2006, 8(1): 19-39. [3] W ANG S A, MENG Q. Liner ship route schedule design with sea contingency time and port time uncertainty [J]. Transportation Research Part B: Methodological, 2012, 46(5): 615-633. [4] W ANG S A, MENG Q. Robust schedule design for liner shipping services [J]. Transportation Research Part E: Logistics and Transportation Review, 2012, 48(6): 1093-1106. [5] LEE C Y, LEE H L, ZHANG J H. The impact of slow ocean steaming on delivery reliability and fuel consumption [J]. Transportation Research Part E: Logistics and Transportation Review, 2015, 76: 176-190. [6] DU J, ZHAO X, W ANG J. Container feeder liner shipping service optimal design model [J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(3): 178-183 (in Chinese). [7] ALIBEYG A, CONTRERAS I, FERN áNDEZ E. Exact solution of hub network design problems with profits [J]. European Journal of Operational Research, 2018, 266(1): 57-71. [8] DU J, ZHAO X, JI M J. Planning model of feeder shipping network for container liners under considering shipper preference [J]. Journal of Traffic and Transportation Engineering, 2017, 17(3): 131-140 (in Chinese). [9] TRAN N K, HAASIS H D. Literature survey of network optimization in container liner shipping [J]. Flexible Services and Manufacturing Journal, 2015, 27(2/3): 139-179. [10] W ANG S A, MENG Q. Reversing port rotation directions in a container liner shipping network [J]. Transportation Research Part B: Methodological, 2013, 50: 61-73. [11] PLUM C E M, PISINGER D, SALAZAR-GONZ áLEZ J J, et al. Single liner shipping service design [J]. Computers & Operations Research, 2014, 45: 1 - 6 . [12] KARIMI H, SETAK M. A bi-objective incomplete hub location-routing problem with flow shipment scheduling [J]. Applied Mathematical Modelling, 2018, 57: 406-431. [13] SUN Z, ZHENG J F. Finding potential hub locations for liner shipping [J]. Transportation Research Part B: Methodological, 2016, 93: 750-761. [14] LIN D Y, HUANG C C, NG M W. The coopetition game in international liner shipping [J]. Maritime Policy & Management, 2017, 44(4): 474-495. [15] RAU P, SPINLER S. Alliance formation in a cooperative container shipping game: Performance of a real options investment approach [J]. Transportation Research Part E: Logistics and Transportation Review, 2017, 101: 155-175. [16] KOZA D F. Liner shipping service scheduling and cargo allocation [J]. European Journal of Operational Research, 2019, 275(3): 897-915. [17] W ANG S A, MENG Q, LIU Z Y. Containership scheduling with transit-time-sensitive container shipment demand [J]. Transportation Research Part B: Methodological, 2013, 54: 68-83. [18] W ANG S A, ALHARBI A, DA VY P. Liner ship route schedule design with port time windows [J]. Transportation Research Part C : Emerging Technologies, 2014, 41: 1-17. [19] ALHARBI A, W ANG S A, DA VY P. Schedule design for sustainable container supply chain networks with port time windows [J]. Advanced Engineering Informatics, 2015, 29(3): 322-331. [20] T A N Z J , W A N G Y D , M E N G Q , e t a l . J o i n t s h i p schedule design and sailing speed optimization for a single inland shipping service with uncertain dam transit time [J]. Transportation Science, 2018, 52(6): 15701588. [21] JIANG X, MAO H J, W ANG Y D, et al. Liner shipping schedule design for near-sea routes considering big customers’ preferences on ship arrival time [J]. Sustainability, 2020, 12(18): 7828. [22] BELL M G H, LIU X, ANGELOUDIS P, et al. A frequency-based maritime container assignment model [J]. Transportation Research Part B: Methodological, 2011, 45(8): 1152-1161. [23] QI X T, SONG D P. Minimizing fuel emissions by optimizing vessel schedules in liner shipping with uncertain port times [J]. Transportation Research Part E: Logistics and Transportation Review, 2012, 48(4): 863-880. [24] BROUER B D, DIRKSEN J, PISINGER D, et al. The Vessel Schedule Recovery Problem (VSRP): A MIP model for handling disruptions in liner shipping [J]. European Journal of Operational Research, 2013, 224(2): 362-374. [25] LI C, QI X T, LEE C Y. Disruption recovery for a vessel in liner shipping [J]. Transportation Science, 2015, 49(4): 900-921. [26] LI C, QI X T, SONG D P. Real-time schedule recovery in liner shipping service with regular uncertainties and disruption events [J]. Transportation Research Part B: Methodological, 2016, 93: 762-788. [27] CHERAGHCHI F, ABUALHAOL I, F ALCON R, et al. Modeling the speed-based vessel schedule recovery problem using evolutionary multiobjective optimization [J]. Information Sciences, 2018, 448/449: 53-74.