水下无人航行器的低能耗动态控位过程,指在满足位姿控制精度要求的前提下,以尽量少的能耗,使UUV自身位姿到达并保持在目标状态,以此延长UUV动态控位的作业时间。面向协同作业的UUV低能耗动态控位是在上述任务的前提下,以UUV集群的阵型保持为基本约束,对其中单体UUV的低能耗动态控位方法及策略进行研究。结合任务约束、通信能力约束、安全距离约束等,分别对其进行位姿保持需求分析。针对面向协同任务阵型保持的UUV,设计低能耗动态控位方法及策略。当UUV距离目标定位点较远时,采用布谷鸟优化方法,为UUV抵达目标定位点附近的过程规划能耗最优的运动方案。结合工程应用背景,设计了仿真案例。试验结果表明,本文提出的策略及控制方法,能够在同样满足预期控制效果的前提下,降低能耗,基本满足UUV集群在面向协同作业任务时的阵型保持要求。
In order to extend the working time of UUV dynamic position control,the low-energy dynamic position control process of the UUV means that under the premise of meeting the accuracy requirements of the position and attitude control, the UUV's own position and attitude can be reached and maintained at the minimum energy consumption. Under the premise of the above-mentioned tasks, the UUV low-energy dynamic position control for cooperative operation takes the formation of the UUV group as the basic constraint, and the low-energy dynamic position control method and strategy of the single UUV is researched. Combined with task constraints, communication capability constraints, safe distance constraints, etc., the posture maintenance requirements were analyzed. A low-energy dynamic position control method and strategy is designed for the UUV oriented to the formation of cooperative tasks. When the UUV is far from the target positioning point, the cuckoo optimization method is used to plan the optimal energy consumption movement plan for the process of UUV reaching the target positioning point. Combining the engineering application background, a simulation case is designed. The test results show that the strategy and control method proposed in this paper can reduce energy consumption while also meeting the expected control effect, and basically meet the formation maintenance of the UUV cluster when facing collaborative tasks.
2020,42(12): 80-85 收稿日期:2020-09-08
DOI:10.3404/j.issn.1672-7649.2020.12.016
分类号:TB566
作者简介:李磊(1987-),男,博士研究生,主要从事水下无人航行器总体与智能技术方向
参考文献:
[1] 肖玉杰, 邱志明, 石章松. UUV国内外研究现状及若干关键问题综述[J]. 电光与控制, 2014, 21(2): 46-49, 89
[2] 陈强. 水下无人航行器[M].北京: 国防工业出版社, 2014.
[3] MORTEN Breivik, STIG Kvaal, PER Østby. From Eureka to K-Pos: Dynamic positioning as a highly successful and important marine control technology[J]. IFAC PapersOnLine, 2015, 48(16)
[4] 朱光文. 我国海洋监测技术研究和开发的现状和未来发展[J]. 海洋技术学报, 2002, 21(2): 27-32
[5] VINCENZO C, HASNES G. Reducing power demand and spikes in dynamic positioning: A model predictive control approach[C]. Oceans, 2015: 1–5.
[6] ZHANG G, CAI Y, ZHANG W. Robust neural control for dynamic positioning ships with the Optimum-Seeking guidance[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2016: 1-10
[7] 曾舒婷. 低能耗长航时水下机器人方案的探究[J]. 机器人技术与应用, 2015(2): 33-36
[8] 曹建春, 李聪. 推力分配优化算法在船舶动态控制中的应用研究[J]. 舰船科学技术, 2017, 39(6): 43-45
[9] 余婷婷. 布谷鸟优化算法在船舶动力定位模拟器推力系统中的应用[J]. 舰船科学技术, 2018, 40(4): 58-60
[10] 刘贵杰, 刘鹏, 穆为磊, 等. 采用能耗最优改进蚁群算法的自治水下机器人路径优化[J]. 西安交通大学学报, 2016, 50(10): 93-98
[11] 高志伟, 代学武, 郑志达. 基于运动控制和频域分析的移动机器人能耗最优轨迹规划[J/OL]. 自动化学报: 1-12[2019-10-18]. https://doi.org/10.16383/j.aas.c180399.
[12] 陈冬. 面向协同观探测的UUV绿色动态控位方法研究[D].哈尔滨: 哈尔滨工程大学, 2020.
[13] DENG J. Optimal algorithm for ship energy consumption based on dynamic distribution[J]. Ship Science and Technology, 2018, 40(22): 23-25
[14] Xin-She YANG, Suash DEB. Cuckoo Search via Lévy flights[C]// 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC). IEEE, 2009.
[15] 郭兆新. 布谷鸟搜索算法研究及其在AUV路径规划中的应用[D].哈尔滨: 哈尔滨工程大学, 2014.
[16] YANG, Xin-She, DEB, Suash. Cuckoo search: recent advances and applications[J]. Neural Computing & Applications, 24(1): 169−174.
[17] YANG X S, DEB S. Cuckoo Search: State-of-the-Art and Opportunities[C]// 2018.
[18] EHSAN Teymourian, VAHID Kayvanfar, GH. M. Komaki Enhanced intelligent water drops and cuckoo search algorithms for solving the capacitated vehicle routing problem[J]. Information Sciences, 2016, 334-335: 354-378
[19] OUAARAB A, AHIOD B, YANG X S. Random-key cuckoo search for the travelling salesman problem[M]. 2015.