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Item type:Publication, Access status: with files , Experimental evaluation of data-driven deterministic wave generation(American Society of Mechanical Engineers (ASME), 2026-06); ; ; ; This paper explores the applicability of data-driven deterministic wave generation in an experimental environment. Various datasets that comprise different combinations of experimental- and synthetic data are used to train a fully convolutional neural network on the task of predicting the wave-maker control signal associated to a desired target wave sequence at a predefined target location in the wave tank. The trained models are evaluated in an experimental program, where the predicted control signals are executed in a wave tank, the actual wave is measured and compared against the predefined target wave. The standard wave modeling approach for the generation of tailored wave sequences, which is based on linear theory, is conducted for reference. The test dataset contains 950 irregular sea states with a wide range of characteristic sea state parameters, as well as seven tailored extreme waves, including the well-known “Draupner Wave”. It is shown that the data-driven approach consistently outperforms the standard method in accuracy across the studied range of sea states, emphasizing its effectiveness as a flexible and precise alternative for wave basin experiments.Publicationtype: Conference Paper29 7 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Data-driven generation of tailored wave sequences(American Society of Mechanical Engineers (ASME), 2024-06); ; ; ; This paper explores the applicability of machine learning techniques for the generation of tailored wave sequences. For this purpose, a fully convolutional neural network was implemented for relating the target wave sequence at the target location in time domain to the respective control signal of the wave board. The database was generated by means of extensive wave tank tests. The experimental campaign focused on the generation of very steep wave groups including wave breaking which cannot be covered by the simplified wave generation methods. The extensive experimental campaign was performed in a small wave tank with an fully automated approach including determination and control of the wave maker motion as well as data measurement. The training data set features wave groups of short duration based on JONSWAP spectra, where the parameters wave steepness, peak wave period and enhancement factor were systematically varied. At the end of the training process, the trained models are able to predict the wave maker control signal based on time series of the target wave defined for a specific target location in the wave tank. The accuracy of the trained models are evaluated by means of unseen validation data. In addition, the predictive accuracy of the trained models is compared with the classical linear transformation approach.Publicationtype: Conference Paper57 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Application of machine learning for the generation of tailored wave sequences(American Society of Mechanical Engineers (ASME), 2022-06); ; ; ; This paper explores the applicability of machine learning techniques for the generation of tailored wave sequences. For this purpose, a fully convolutional neural network was implemented for relating the target wave sequence at the target location in time domain to the respective wave sequence at the wave board. The synthetic training and validation data were acquired by applying the high-order spectral (HOS) method. The HOS method is a very accurate method for modeling non-linear wave propagation and its numerical efficiency allows the generation of large synthetic data sets. The training data featured wave groups of short duration based on JONSWAP spectra. The sea state parameters wave steepness, wave period and enhancement factor were systematically varied. At the end of the training process, the trained models were able to predict the wave sequences at the wave board based on the time series of the target wave defined for a specific target location in the wave tank. The accuracy of the trained models were evaluated by means of unseen validation data. In addition, the predictive accuracy of the trained models was compared with the classical linear transformation approach.Publicationtype: Conference Paper73 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Workshop: Adaptive Structures At Shore 2022 : 23rd & 24th of May 2022, Hamburg, Germany : conference proceedings(Technische Universität Hamburg, 2022-05-23); ; ; ; Adaptive structures at shore provide an opportunity to minimize the resource consumption and the impact on the marine ecosystems through an efficient adaptation to changing boundary conditions. A combination of impact prediction, monitoring and material resistance estimation, numerical modelling, and innovative design is the key to success. The workshop “Adaptive Structures at Shore 2022” hosted by Hamburg University of Technology (TUHH) brings together international scientists and professionals to discuss the latest developments in the field and to explore synergies.Publicationtype: Conference Proceedings311 174 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Towards coupling land-based and water-based mobile robots for monitoring and inspection of waterside structures(2022-05-23); ; ; ; Waterside structures represent vulnerable and often safety-relevant land/water interfaces that fulfill critical operations, such as facilitating commute across natural impediments or protecting and managing marine traffic. Therefore, monitoring and inspection of waterside structures is essential to ensure continuous operation. With recent technological advancements, robotic systems have been devised to execute monitoring and inspection tasks. However, robotic monitoring and inspection of waterside structures, as compared to monitoring and inspection of land-based civil infrastructure systems, is associated with a number of challenges because of the specifics of waterside structures. In particular, the collaboration of land-based and water-based robots must be ensured, which, so far, has received little attention in robotics research. This paper proposes a concept to couple land-based and water-based robots for monitoring and inspection of waterside structures. The concept is centered around collaborative analysis of the structural condition, and it builds upon embedded computing considering the semantics provided by digital twins and building information models. Finally, research directions are discussed that need to be investigated for transferring the concept into engineering practice.Publicationtype: Conference Paper131 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Real-time trajectory tracking control of a parallel robot with flexible linksReal-time end-effector trajectory tracking is applied to a newly developed parallel robotic system with two highly flexible links. In contrast to previous works, which are typically based on offline precalculations or rigid model inversion, a dynamic flexible multibody model is inverted online. As the underactuated system is non-minimum phase, the inverse model needs to be rendered stable for real-time integration. Therefore, three approaches are developed for the considered robot. Firstly, output redefinition is applied by directly weighting the elastic deformations and rotations of the links. Secondly, a small counter weight is attached at an advantageous location on the robot having only a minor influence on the eigenfrequencies. Lastly, the rotational degree of freedom of the end-effector is used to stabilize the internal dynamics of the inverse model with a small motion of a rotary motor. The concepts and real-time applicability are validated within experiments which extends the related literature being highly based on theoretical investigations. The experimental end-effector tracking performance based on the three minimum phase approaches is close to the desired trajectory and clearly outperforms classical rigid body inversion.Publicationtype: Journal Article230 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Theoretical and experimental study of a pendulum excited by random loads(2019-10-01); Results on the behaviour of a pendulum which is parametrically excited by large amplitude random loads at its pivot are presented, including a novel experimental case study. Thereby, it is dealt with a random excitation by a non-white Gaussian stochastic process with prescribed spectral density. Special focus is devoted to stochastic processes resulting from random sea wave elevation and the question whether random sea waves can lead to rotational motion of the parametrically excited pendulum. The motivation for such an experimental study is energy harvesting from ocean waves.Publicationtype: Journal Article55 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Modeling and Trajectory Tracking Control of a New Parallel Flexible Link Robot(2018-12-27); ; ; A completely new compliant lightweight robot is presented with a kinematic loop and a highly flexible link. It is explained how to model such parallel robots accurately but still computationally efficient. The elastic deformations are described with the floating frame of reference approach. For the flexible components this allows to use linear finite element models, which can represent arbitrary geometries. These models are further reduced by modal truncation and a Component Mode Synthesis minimizing the number of elastic degrees of freedom, which is necessary for real-time control purposes. The obtained model of the underactuated robot is non-minimum phase for the end-effector as output. Thus, for the applied trajectory tracking controller which is based on servo constraints, the concept of stable inversion is used. The performance is compared to a relocated minimum phase output. Corresponding simulations are validated by first experimental results showing the need for and high accuracy of the flexible model and the trajectory tracking control.Publicationtype: Conference Paper200 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Design and adaptive depth control of a micro diving agent(IEEE, 2017-06-09); ; ; ; This letter presents the depth control of an autonomous micro diving agent called autonomous diving agent (ADA). ADA consists of off-The-shelf components and features open-source hardware and firmware. It can be deployed as a testbed for depth controllers, as well as a mobile sensor platform for research or in industrial tanks.We introduce a control law that is based on the feedback linearization method and enhanced by an adaptive fuzzy algorithm to cope with modeling inaccuracies. The proposed depth controller is computationally light enough to run on ADAs embedded hardware. In experiments performed in a wave tank, the adaptive fuzzy scheme shows the ability to deal with both depth regulation and depth profile tracking. ADA is even able to hold on to dynamic isobars despite external disturbances. We demonstrate that under the influence of waves, ADA describes orbital motions similar to water particles.Publicationtype: Journal Article114 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Embedded spherical localization for micro underwater vehicles based on attenuation of electro-magnetic carrier signals(Multidisciplinary Digital Publishing Institute (MDPI), 2017-04-26); ; ; ; Self-localization is one of the most challenging problems for deploying micro autonomous underwater vehicles (μAUV) in confined underwater environments. This paper extends a recently-developed self-localization method that is based on the attenuation of electro-magnetic waves, to the μAUV domain. We demonstrate a compact, low-cost architecture that is able to perform all signal processing steps present in the original method. The system is passive with one-way signal transmission and scales to possibly large μAUV fleets. It is based on the spherical localization concept. We present results from static and dynamic position estimation experiments and discuss the tradeoffs of the system.Publicationtype: Journal ArticleScopus© Citations 17 710 671