深海集矿机控制系统设计与实现
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摘要
深海采矿是一个极其复杂的采集过程,集矿机控制系统则是整个深海采矿系统的关键。我国在“八五”、“九五”时期开发的深海集矿机控制系统采用了主从式控制模式。其决策效率低,决策效果不理想,而且集矿机液压系统的控制存在着稳定性欠佳,控制效果差的问题。
     本文以深海集矿机集矿过程为背景,介绍了深海集矿机控制系统的研究和设计。首先在深海采矿和集矿机集矿工艺过程分析的基础上,分析集矿过程控制系统中要解决主要问题,介绍了研究方法,给出了集矿机控制系统设计的总体设计。针对集矿机工作要求,设计了集矿机路径规划系统,给出了集矿机的全局路径规划和局部动态路径规划。针对集矿机履带液压系统的非线性、大惯性、时滞性和复杂的深海环境,本文利用模糊控制系统的理论,分析了常规模糊控制器对履带液压系统的角位移控制性能,利用模糊优化方法,设计了液压系统的规则自修正的模糊控制器,并利用在线学习算法对模糊控制器的参数进行了优化,仿真结果表明算法对角位移控制的良好性能;同时采用模糊控制器积分改进实现了转速控制的良好性能。
     并用VC编制了监控子系统,内容包括各种输入/输出程序,通信接口程序,各种数据的处理、存储、显示和打印,动态监控画面,有关设备的操作顺序、连锁、保护控制程序,仿真测试表明软件系统能够正常运行。
Deep-sea mining process is a very intricate process. The control subsystem of deep-sea collector is the key problem in the whole mining system. Principle - subordinate control mode is adopted in the deep-sea mining system in our country during the Eighth and Ninth five-year Plan. It's common that the efficiency of their decision is very low. Furthermore, the steadiness and effect of the hydraulic pressure control system of deep-sea mining collector cannot meet the control demands.
    In this paper, the control system of deep-sea collector is studied and devised. Firstly, based on the analysis of the technics of mining process and the collector's working process, the problems in the control system of deep-sea collector including collector path planning and control algorithm are briefly analyzed, the studying methods for them are introduced, and then the whole architecture is given in detail. Secondly, according to the working demands of collector, a path planning system is designed, which can complete the global path planning and the local path planning. Thirdly, fuzzy control algorithms are studied for the control of hydraulic system of collector's tracks. The emulational results indicate that the fuzzy algorithm is effective and desirable.
    At last, the system software is expatiated in its structures and functions. Here, the thoughts and the methods of the system design are emphasized. The system software, developed under the development of Visual C++ 6.0, comprises a main monitoring module, a communication module, database module, input and output module of control panel and so on. The precision and reliability is being proved.
引文
[1] Holser A E. Manganese Nodules Resource and Minning Site Availability. Washington D C: Professional Staff Study Ocean Mining Administration, U.S.Department of the Intetior, 1976
    [2] Cronan D S. Under Water Mineral. London: Academic Press, 1980, 362-378
    [3] Graham T M. Assessment of manganese nodules resource. Seabed Mineral, 1982, 1: 1-79
    [4] Willing C.G. An advanced design deep sea mining system. Proc.of Offshore Technology, Conference Houston, 1981: 247-255
    [5] Thiel H. Deep—sea mining: A challenge for marine scientists. Proc. OfⅩⅤ World Mining Congress, Madrid(spain), 1992:1321-1330
    [6] Bath A.R. Deep sea mining technology: Recent developments and future projects. Mining Engineering, 1991, 43 (1): 125-128
    [7] Earney F.C.E. Technology and economics of deep seabed minerals. Deep seabed policy and minerals. Ocean Management and Policy Series, 1990:94-123
    [8] Thiel, Hjalmar. Evaluation of the environmental consequences of polymetallic nodule mining based on the results of the TUSCH Research Association. Deep Sea Research Part2: Topical Studies in Oceanography, 2001, 48 (17-18): 3433-3452
    [9] 王随平等.自行式海底作业车设计.2000
    [10] 陈仁际,吴镇炜,王韬,谈大龙.分布式多机器人装配系统任务合作规划算法研究.中国机械工程,2000,4(4):418-421
    [11] 胡虹.智能水下机器人任务规划专家系统研究.计算机与数字工程,1998,26(1):30-34
    [12] 邵鹏鸣,李成刚,吴翰声.基于对象模型的YGR-1机器人智能任务规划与控制.中国机械工程,2001,12(4):451-455
    [13] 邵鹏鸣,李成刚,吴翰声.基于对象模型的不确定环境下服务性机器人系统模型研究.机械工程学报,2001,37(8):47-51
    [14] 庄晓东,孟庆春,殷波等.动态环境中基于模糊概念的机器人路径搜索方法.机器人,2001,23(5):397-399
    [15] 李彩虹,张景元,李贻斌.基于模糊控制的移动机器人的路径规划.淄博学
    
    院学报,2001,3(3):27-30
    [16] Hart mut, Jrg Huser, Jens Wehking. Path planning for a fuzzy controlled autonomous mobile robot. Fifth IEEE Int. Conf, On Fuzzy Systems Fuzz-IEEE'96[c]. UAS: New Orleans, 1996
    [17] 禹建丽,韩平.一种基于神经网络的机器人路径规划算法.洛阳工学院学报,2001,22(1):31-34
    [18] 陈宗海,陈锋.一种不确定环境下移动机器人避障规划算法.机器人,2002,24(4):359-361
    [19] 禹建丽,成久洋子,Valeri Kroumov.线性再励的自适应变步长机器人神经网络路径规划算法.燕山大学学报,2002,26(3):259-266
    [20] 孙树栋,曲彦宾.遗传算法在机器人路径规划中的应用研究.西北工业大学学报,1998,16(1):79-83
    [21] Kazuo Sugibara, John Smith. Genetic algorithms for adaptive motion planning of an autonomous mobile robots[A]. Problems IEEE Trans SMC[C]. USA: SIM, 1997
    [22] 周明,孙树彦,彭炎午.用遗传算法规划机器人路径.西北工业大学学报,1998,16(4):581-583
    [23] 周明,孙树彦,彭炎午.基于遗传模拟退火算法的机器人路径规划.航空学报,1998,19(1):119-120
    [24] 景兴建,王越超.一种基于理性遗传算法的协调运动行为合成算法.机器人,2002,24(1):49-54
    [25] Cai Z X, Peng Z H. Cooperative coevolutionary adaptive genetic alglrithm in path planning of cooperative multi-mobile robot system. Journal of Intelligent and Robots System, 2002, 4 (33): 61-71
    [26] 孙树栋,林茂.基于遗传算法的多移动机器人协调路径规划.自动化学报,2002,26(5):673-676
    [27] Tsoukalas L H, Houstis EN, Jones Gv. Neurofuzzy motion planners for intelligent robot. Journal of Intelligent and Robotic Systems, 1997, 19: 339-356
    [28] 张颖,吴成东,原宝龙.机器人路径规划方法综述.控制工程,2003,10(增):152-155
    [29] 张森,张正亮.MATLAB仿真技术与实例应用教程.机械工业出版社,2004.1
    [30] 何玉彬,李新忠.神经网络控制技术及其应用.科学出版社, 2000
    [31] 从爽.神经网络、模糊系统及其在运动控制中的应用.合肥:中国科学技术大学出版社,2001[14]俞瑞钊,史济建.人工智能原理与技术.浙江大学出
    
    版社,1993
    [32] 刘代军等.自修正量化因子和比例因子的复合模糊控制器设计.弹箭与制导学报,2001,21(1):25-28,31
    [33] 甄敏,袁艳,张泰山.三维控制规则自修正模糊算法的研究.计算技术与自动化,2000,19(1):16-18
    [34] 袁艳,张泰山.一种通用模糊控制器的研究与设计.计算技术与自动化,2003,22(2):25-27,38
    [35] 顾临怡,王庆丰,袁卫军.电液比例位置控制系统的自学习模糊控制.机械与液压,1995,(6):315-318
    [36] 张晓龙,徐树梅,夏路易.基于参数自调整的三维模糊控制系统.测试技术学报,2003,17(3):274-277
    [37] 郭爽,边立秀,张玉萍.循环流化床锅炉的燃烧控制系统.电力情报,2002,4:8-10
    [38] H.E.梅里特.液压控制系统.科学出版社,1976
    [39] Merritt,H.E., J.T.Gavin. Friction Load on Hydraulic Servos. Proc. Natl. Conf. Indl. Hydraulics, 16, 1962, 174
    [40] 孙文质.液压控制系统.国防工业出版社,1985
    [41] 张治立.液压控制系统.国防工业出版社,1981
    [42] 诸静等.模糊控制原理与应用.北京:机械工业出版社,1995.7
    [43] 赵振宇,徐用懋.模糊理论和神经网络的基础与应用.北京、南宁:清华大学出版社、广西科学技术出版社,1997.8
    [44] 张国良,曾静,柯熙政,邓方林.模糊控制及其MATLAB应用.西安:西安交通大学出版社,2002.11
    [45] Mansor MA, Morris AS. Path planning in unknown environment with obstacles using virtual windows. Journal of Intelligent and Robotic Systmes, 1999, 14(24): 235-251
    [46] Zavlangas PG, Tzafestas SG, Industrial robot navigation and obstacle avoidance employing fuzzy logic. Journal of Intelligent and Robotic Systems, 2002, 6(27): 85-97
    [47] 薛定宇,陈阳泉.基于MATLAB/Simulink的系统仿真技术与应用.清华大学出版社,2002.4