锚泊辅助动力定位系统预测控制
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摘要
针对锚泊辅助动力定位系统预测控制中的实时性特征,本文选择隐式广义预测控制算法对混合定位船舶控制器进行设计。对于广义预测控制算法滚动优化过程中需引入繁琐的丢备图方程中引起的实时性差问题,采用隐式自校正方法进行了修正。通过利用递推最小二乘法直接辨识控制增量表达式中的参数,减少了计算复杂度,从而满足了控制器的实时性要求。隐式GPC不仅具有传统的GPC算法的优点,而且鲁棒性更好。通过船舶混合定位系统的仿真结果可以看出,隐式GPC算法具有良好的性能,提高了混合定位系统的定位精度和性能。
In this paper,the real-time issues in predictive control of thruster-assisted mooring positioning systems is studied,and an implicit general predictive control algorithm was used to design the controller for the hybrid positioning vessel. The real-time problem caused by the cumbersome diophantine equation in the global process control( GPC) rolling optimization process was modified by an implicit self-tuning method. The parameters in the control incremental expression were directly identified by the recursive least squares method,which greatly reduced the computational complexity,so as to meet the real-time requirements of the controller. Results showed that the implicit GPC not only has the advantages of traditional GPC algorithms,but also has better robustness. It can be seen from the simulation results of the ship hybrid positioning system that the implicit GPC algorithm has better performance,improving the positioning accuracy and performance of the hybrid positioning system.
In this paper,the real-time issues in predictive control of thruster-assisted mooring positioning systems is studied,and an implicit general predictive control algorithm was used to design the controller for the hybrid positioning vessel. The real-time problem caused by the cumbersome diophantine equation in the global process control( GPC) rolling optimization process was modified by an implicit self-tuning method. The parameters in the control incremental expression were directly identified by the recursive least squares method,which greatly reduced the computational complexity,so as to meet the real-time requirements of the controller. Results showed that the implicit GPC not only has the advantages of traditional GPC algorithms,but also has better robustness. It can be seen from the simulation results of the ship hybrid positioning system that the implicit GPC algorithm has better performance,improving the positioning accuracy and performance of the hybrid positioning system.
引文
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[15]HE W,ZHANG S,GE S S. Robust adaptive control of a thruster assisted position mooring system[J]. Automatica,2014,50(7):1843-1851.
[16]DU J,HU X,SUN Y. Robust dynamic positioning of ships with disturbances under input saturation[J]. Automatica,2016,73(C):207-214.
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[18]VEKSLER A V,JOHANSEN T A,BORRELLI F,et al.Dynamic positioning with model predictive control[J].IEEE transactions on control systems technology,2016,24(4):1340-1353.
[19]TSOPELAKOS A,PAPADOPOULOS E. Design and evaluation of dynamic positioning controllers with parasitic thrust reduction for an overactuated floating platform[J].IEEE transactions on control systems technology,2016,25(1):145-160.
[20]SERGIO V,JOSE R,MARCO R,et al. Model predictive control for power converters and drives:advances and trends[J]. IEEE transactions on industrial electronics,2017,64(2):935-947.
[21]ZHENG Y,LI S E,LI K,et al. Distributed model predictive control for heterogeneous vehicle platoons under unidirectional topologies[J]. IEEE transactions on control systems technology,2017,(99):1-12.
[2]NGUYEN D T,SRENSEN A J. Switching control for thruster-assisted position mooring[J]. Control engineering practice,2009,17(9):985-994.
[3]REN Zhengru,SKJETNE R,HASSANI V. Supervisory control of line breakage for thruster-assisted position mooring system[J]. IFAC-PapersOnline,2015,48(16):235-240.
[4]雷正玲,郭晨,刘正江.船舶锚泊辅助动力定位的抗扰控制[J].哈尔滨工程大学学报,2015,36(1):24-28.LEI Zhengling,GUO Chen,LIU Zhengjiang. Disturbance rejection control over ship thruster-assisted mooring positioning[J]. Journal of Harbin Engineering University,2015,36(1):24-28.
[5]KWON H,KIM N. Neural network based adaptive control for a thruster assisted mooring vessel in mooring line failure[C]//Offshore Technology Conference Asia. Kuala Lumpur,Malaysia:OTC,2016:1-15.
[6]AAMO O M,FOSSEN T I. Controlling line tension in thruster assisted mooring systems[C]//Proceedings of 1999IEEE International Conference on Control Applications.Kohala Coast,HI,USA:IEEE,2007:1104-1109.
[7]BERNTSEN P I B,AAMO O M,LEIRA B J. Ensuring mooring line integrity by dynamic positioning:controller design and experimental tests[J]. Automatica, 2009,45(5):1285-1290.
[8]HAN P,LI C,DONG Z. Study of networked control systems based on implicit generalized predictive control algorithm1[C]//Proceedings of the 8th World Congress on Intelligent Control and Automation. Ji'nan,China:IEEE,2010:4301-4306.
[9]MARCHETTI A G,FERRAMOSCA A,GONZLEZ A H.Steady-state target optimization designs for integrating realtime optimization and model predictive control[J]. Journal of process control,2014,24(1):129-145.
[10]PATRINOS P,BEMPORAD A. An accelerated dual gradient-projection algorithm for embedded linear model predictive control[J]. IEEE transactions on automatic control,2014,59(1):18-33.
[11]BARREIROS J A L,SILVA A S E,COSTA A J A S. A self-tuning generalized predictive power system stabilizer[J]. International journal of electrical power&energy systems,1998,20(3):213-219.
[12]叶东,张宗增,吴限德.有推力约束航天器交会的模型预测制导策略[J].哈尔滨工程大学学报,2017,38(5):759-765.YE Dong,ZHANG Zongzeng,WU Xiande. Model predictive based guidance strategy for spacecraft rendezvous with thrust constraints[J]. Journal of Harbin Engineering University,2017,38(5):759-765.
[13]周卫东,郑兰,廖成毅,等.多重状态时滞系统的minmax鲁棒预测控制[J].哈尔滨工程大学学报,2016,37(12):1685-1690.ZHOU Weidong,ZHENG Lan,LIAO Chengyi,et al. Min-max robust predictive control for multi-state time-delay systems[J]. Journal of Harbin Engineering University,2016,37(12):1685-1690.
[14]苏义鑫,赵俊.带有UKF滚动时域估计的动力定位控制器[J].哈尔滨工程大学学报,2016,37(10):1381-1386,1393.SU Yixin,ZHAO Jun. Dynamic positioning controller with UKF moving horizon estimation[J]. Journal of Harbin Engineering University,2016,37(10):1381-1386,1393.
[15]HE W,ZHANG S,GE S S. Robust adaptive control of a thruster assisted position mooring system[J]. Automatica,2014,50(7):1843-1851.
[16]DU J,HU X,SUN Y. Robust dynamic positioning of ships with disturbances under input saturation[J]. Automatica,2016,73(C):207-214.
[17]HU X,DU J,SUN Y. Robust adaptive control for dynamic positioning of ships[J]. IEEE journal of oceanic engineering,2017,(99):1-10.
[18]VEKSLER A V,JOHANSEN T A,BORRELLI F,et al.Dynamic positioning with model predictive control[J].IEEE transactions on control systems technology,2016,24(4):1340-1353.
[19]TSOPELAKOS A,PAPADOPOULOS E. Design and evaluation of dynamic positioning controllers with parasitic thrust reduction for an overactuated floating platform[J].IEEE transactions on control systems technology,2016,25(1):145-160.
[20]SERGIO V,JOSE R,MARCO R,et al. Model predictive control for power converters and drives:advances and trends[J]. IEEE transactions on industrial electronics,2017,64(2):935-947.
[21]ZHENG Y,LI S E,LI K,et al. Distributed model predictive control for heterogeneous vehicle platoons under unidirectional topologies[J]. IEEE transactions on control systems technology,2017,(99):1-12.