基于正交神经网络的动力定位自适应控制器设计
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摘要
动力定位船舶在海上进行定点定位作业时不可避免地会受到风、浪、流海洋环境力的干扰,这种持续的环境力扰动会导致船舶在实际定位时产生静态误差,不能准确地到达目标定位点.在利用反步法进行控制器设计时,大多数文献引入自适应积分项用于抵抗外界环境扰动,通过在控制器中加入船舶当前位置与目标位置的偏差积分项估计出外界未知环境扰动,从而达到自适应控制效果.在此基础上,改进了自适应积分项为正交基神经网络项进行控制器设计,以补偿静态误差,实现准确定位.最后通过水池模型试验对比验证了所设计控制器的可行性.
Dynamic positioning ship will inevitably be disturbed by marine environmental forces such as wind, waves and current which cause static errors in the actual positioning of the ship. When using the backstepping method to design the controller, most of the documents introduce adaptive integral to resist the environmental load interference. By adding the deviation integral of the current position of the ship and the target position in the controller, the unknown environment disturbance is estimated, thus the adaptive control effect is achieved. On this basis, the adaptive integral term is improved for the orthogonal basis neural network to design the controller and the purpose of eliminating static deviation and realizing the accurate positioning is achieved. Finally, the feasibility of the designed controller is verified through the comparison of model experiments.
Dynamic positioning ship will inevitably be disturbed by marine environmental forces such as wind, waves and current which cause static errors in the actual positioning of the ship. When using the backstepping method to design the controller, most of the documents introduce adaptive integral to resist the environmental load interference. By adding the deviation integral of the current position of the ship and the target position in the controller, the unknown environment disturbance is estimated, thus the adaptive control effect is achieved. On this basis, the adaptive integral term is improved for the orthogonal basis neural network to design the controller and the purpose of eliminating static deviation and realizing the accurate positioning is achieved. Finally, the feasibility of the designed controller is verified through the comparison of model experiments.
引文
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[10]徐海祥,瞿洋,余文曌. 船舶动力定位反步逆最优控制 [J]. 大连理工大学学报, 2017, 57(1): 46-54. XU Haixiang, QU Yang, YU Wenzhao. Inverse optimal backstepping control of dynamic positioning ships [J]. Journal of Dalian University of Technology, 2017, 57(1): 46-54. (in Chinese)
[11]袁耀. 基于自适应模糊控制的船舶动力定位控制器设计研究 [D]. 大连: 大连海事大学, 2013. YUAN Yao. The design and study of dynamic positioning controller of ship based on adaptive fuzzy control [D]. Dalian: Dalian Maritime University, 2013. (in Chinese)
[12]刘洋. 船舶动力定位的智能控制及推力分配研究 [D]. 大连: 大连海事大学, 2013. LIU Yang. Research on intelligent control and thrust distribution for ship dynamic positioning [D]. Dalian: Dalian Maritime University, 2013. (in Chinese)
[13]ZHANG Lijun, QI Xue, PANG Yongjie. Adaptive output feedback control based on DRFNN for AUV [J]. Ocean Engineering, 2009, 36(9/10): 716-722.
[14]DU Jialu, YANG Yang, WANG Dianhui, et al. A robust adaptive neural networks controller for maritime dynamic positioning system [J]. Neurocomputing, 2013, 110: 128-136.
[15]YU Wenzhao, XU Haixiang, FENG Hui. Robust adaptive fault-tolerant control of dynamic positioning vessel with position reference system faults using backstepping design [J]. International Journal of Robust and Nonlinear Control, 2018, 28(2): 403-415.
[16]YANG S S, TSENG C S. Orthogonal neural network for function approximation [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 1996, 26(5): 779-785.
[17]ZHANG Xinguang, ZOU Zaojian. Black-box modeling of ship manoeuvring motion based on feed-forward neural network with Chebyshev orthogonal basis function [J]. Journal of Marine Science and Technology (Japan), 2013, 18(1):42-49.
[18]赵小仨,徐海祥. 非线性水动力导数的数值计算与研究 [J]. 武汉理工大学学报(交通科学与工程版), 2017, 41(1): 70-75,81. ZHAO Xiaosa, XU Haixiang. Numerical simulation and research of nonlinear hydrodynamic derivatives [J]. Journal of Wuhan University of Technology (Transportation Science & Engineering), 2017, 41(1): 70-75,81. (in Chinese)
[2]FOSSEN T I. Handbook of Marine Craft Hydrodynamics and Motion Control [M]. New York: John Wiley & Sons, 2011.
[3]BALCHEN J G, JENSSEN N A, MATHISEN E, et al. Dynamic positioning of floating vessels based on Kalman filtering and optimal control [J]. Proceedings of the IEEE Conference on Decision and Control, 1980, 2:852-864.
[4]BALCHEN J G, JENSSEN N A, ELDAR M, et al. A dynamic positioning system based on Kalman filtering and optimal control [J]. Modeling, Identification and Control, 1980, 1(3): 135-163.
[5]FOSSEN T I, GROVLEN A. Nonlinear output feedback control of dynamically positioned ships using vectorial observer backstepping [J]. IEEE Transactions on Control Systems Technology, 1998, 6(1): 121-128.
[6]YANG Yang, DU Jialu, LI Guangqiang, et al. Dynamic surface control for nonlinear dynamic positioning system of ship [J]. Advances in Intelligent and Soft Computing, 2012, 125 AISC: 237-244.
[7]刘芙蓉. 半潜船动力定位控制系统建模和仿真研究 [D]. 武汉: 武汉理工大学, 2011. LIU Furong. Research on modeling and simulating of dynamic positioning control system of the semi-submersible vessel [D]. Wuhan: Wuhan University of Technology, 2011. (in Chinese)
[8]李和贵. 船舶动力定位系统的智能控制与鲁棒控制 [D]. 上海: 上海交通大学, 2003. LI Hegui. Ship dynamic positioning system using intelligent control and robust control [D]. Shanghai: Shanghai Jiao Tong University, 2003.(in Chinese)
[9]卢林枫,余文曌. 基于Backstepping的船舶动力定位鲁棒控制器设计 [C]// 聚焦应用支撑创新——船舶力学学术委员会测试技术学组2016年学术会议论文集. 武汉: 中国造船工程学会, 2016: 206-212. LU Linfeng, YU Wenzhao. Design of backstepping controller for nonlinear ship dynamic position [C]// Focused Application Supporting Innovation — Papers Collection of 2016 Academic Conference of the Test Technology Group of the Academic Committee of Ship Mechanics. Wuhan: Chinese Society of Naval Architecture and Marine Engineering, 2016: 206-212. (in Chinese)
[10]徐海祥,瞿洋,余文曌. 船舶动力定位反步逆最优控制 [J]. 大连理工大学学报, 2017, 57(1): 46-54. XU Haixiang, QU Yang, YU Wenzhao. Inverse optimal backstepping control of dynamic positioning ships [J]. Journal of Dalian University of Technology, 2017, 57(1): 46-54. (in Chinese)
[11]袁耀. 基于自适应模糊控制的船舶动力定位控制器设计研究 [D]. 大连: 大连海事大学, 2013. YUAN Yao. The design and study of dynamic positioning controller of ship based on adaptive fuzzy control [D]. Dalian: Dalian Maritime University, 2013. (in Chinese)
[12]刘洋. 船舶动力定位的智能控制及推力分配研究 [D]. 大连: 大连海事大学, 2013. LIU Yang. Research on intelligent control and thrust distribution for ship dynamic positioning [D]. Dalian: Dalian Maritime University, 2013. (in Chinese)
[13]ZHANG Lijun, QI Xue, PANG Yongjie. Adaptive output feedback control based on DRFNN for AUV [J]. Ocean Engineering, 2009, 36(9/10): 716-722.
[14]DU Jialu, YANG Yang, WANG Dianhui, et al. A robust adaptive neural networks controller for maritime dynamic positioning system [J]. Neurocomputing, 2013, 110: 128-136.
[15]YU Wenzhao, XU Haixiang, FENG Hui. Robust adaptive fault-tolerant control of dynamic positioning vessel with position reference system faults using backstepping design [J]. International Journal of Robust and Nonlinear Control, 2018, 28(2): 403-415.
[16]YANG S S, TSENG C S. Orthogonal neural network for function approximation [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 1996, 26(5): 779-785.
[17]ZHANG Xinguang, ZOU Zaojian. Black-box modeling of ship manoeuvring motion based on feed-forward neural network with Chebyshev orthogonal basis function [J]. Journal of Marine Science and Technology (Japan), 2013, 18(1):42-49.
[18]赵小仨,徐海祥. 非线性水动力导数的数值计算与研究 [J]. 武汉理工大学学报(交通科学与工程版), 2017, 41(1): 70-75,81. ZHAO Xiaosa, XU Haixiang. Numerical simulation and research of nonlinear hydrodynamic derivatives [J]. Journal of Wuhan University of Technology (Transportation Science & Engineering), 2017, 41(1): 70-75,81. (in Chinese)