Modelling and Control of ASV acting as communication node for deep-sea applications

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• 作者：Martin Kurowski^* ; ^{martin.kurowski@uni-rostock.de" class="auth_mail" title="E-mail the corresponding author} ; Erik Rentzow^* ; Sebastian Ritz^** ; ^{sebastian.ritz@tu-berlin.de" class="auth_mail" title="E-mail the corresponding author} ; Bernhard Lampe^* ; Torsten Jeinsch^*
• 关键词：Autonomous surface vehicles ; modelling ; parameterization ; feed forward control
• 刊名：IFAC-PapersOnLine
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：49
• 期：23
• 页码：291-296
• 全文大小：1079 K

文摘

This paper describes the basic characteristics of a six degrees of freedom dynamic model of an innovative ocean-going unmanned surface vehicle. The model is used in an explicit and implicit way to ensure the operation of the autonomously acting vehicle, which serves as communication node between surface and underwater parts of a complex deep-sea monitoring system. In practice, it is a cumbersome task to identify the unknown parameters of such nonlinear models, due to strong couplings of the motion variables, measurement noise and unknown disturbances. In order to parameterize the models, special maneuvers have been carried out to decouple the motions and identify the corresponding parameters. Properties of the acoustic communication has been taken into account when designing the unmanned surface vehicle. Finally, it has been built as a shallow submerged vehicle with water surface-piercing towers to assure a reliable acoustic communication and positioning link up to a depth of 6.000 meters even in heavy sea states. As the vehicle motion has a decisive impact on its operation, the basic characteristics of the motion of the vehicle in waves have been investigated from the quasi-static case using potential theory to simpler dynamic models for the specific degree of freedom. Further, these models are used to design feed forward and feedback controller to ensure the autonomous vehicle operation. The paper concludes with a performance evaluation of the proposed controllers based on data recorded at field trials.