无模型自适应控制算法在ROV定深控制中的仿真研究
Simulation Research of Model-Free Adaptive Control Algorithm in ROV Depth Control
投稿时间:2018-12-19  修订日期:2019-10-26
DOI:
中文关键词:  无模型自适应控制  ROV  定深控制  数据驱动
英文关键词:model-free adaptive control (MFAC)  Remote Operated Vehicle(ROV)  depth control  data driven
基金项目:山东省自然科学基金
作者单位E-mail
宋大雷 中国海洋大学 daleisong@126.com 
路宁 中国海洋大学  
周丽芹 中国海洋大学 liqin72@126.com 
李坤乾 中国海洋大学  
杨华 中国海洋大学  
王红都 中国海洋大学  
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中文摘要:
      将无模型自适应控制方法应用于ROV(Remote Operated Vehicle)定深控制当中。该控制方案的设计仅利用ROV的垂向推力输入数据和深度输出数据,用动态线性化时变模型替代ROV非线性系统模型,算法中不包含ROV模型及水动力参数信息。因此,解决了ROV因系统复杂,水动力参数难以确定所导致的控制器设计复杂度高,控制效果不理想的问题。为了便于仿真,本文建立含有补偿参数的ROV简化模型,模型仅用于产生系统的I/O数据,不参与控制器的设计。仿真结果表明,在ROV定深控制当中,无模型自适应控制(Model-free adaptive control,MFAC)比PID控制具有更强的抗扰能力。此外,在欠阻尼ROV系统中,基于偏格式动态线性化的无模型自适应控制(partial form dynamic linearization based Model-free adaptive control,PFDL-MFAC)方案相比于基于紧格式动态线性化的无模型自适应控制(compact form dynamic linearization based Model-free adaptive control,CFDL-MFAC)方案具有更好的控制效果。
英文摘要:
      The model-free adaptive control method is applied to the ROV (Remote Operated Vehicle) depth control. The design of the control scheme only uses the ROV vertical thrust input data and depth output data, and the ROV nonlinear system model is replaced with a dynamic linearized time-varying model. The ROV model and hydrodynamic parameter information are not included in the algorithm. Therefore, the problem that the ROV is complicated due to the complexity of the system and the hydrodynamic parameters are difficult to determine is high in complexity and the control effect is not ideal. Therefore, it solves the problem that the ROV controller is complicated due to the complexity of the system and the hydrodynamic parameters are difficult to determine. Then, it can Improve control effect. In order to facilitate the simulation, this paper establishes a simplified model of ROV with compensation parameters. The model is only used to generate I/O data of the system and does not participate in the design of the controller. The simulation results show that Model-free adaptive control (MFAC) has stronger anti-interference ability than PID in ROV depth control. In addition, in the under-damped ROV system, the partial form dynamic linearization based model-free adaptive control (PFDL-MFAC) scheme is compared to the compact form dynamic linearization based Model-free adaptive control (CFDL-MFAC) scheme has better control effects.
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