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Item type:Publication, Access status: without files , Dynamic model inversion of a soft robot using servo-constraintsDue to their flexibility, soft robots offer several advantages over their rigid counterparts in applications such as sensitive manipula-tion and human-machine interaction. However, their use in agile tasks is currently limited because dynamic control of soft robots is usually dif-ficult due to large elastic deformations, underactuation, and redundant actuation. The piecewise constant curvature (PCC) model is commonly used for dynamic forward modeling of soft robots. However, analytical inversion of forward dynamics is typically infeasible due to the model’s complexity. For control purposes, though, inverse models are necessary. In this work, we propose obtaining t he inverse dynamic model of a soft robot from the forward dynamic PCC model using the servo-constraint approach. We analyze the system’s internal stability and validate the model using a physical soft robot. Experimental results demonstrate the effectiveness of our approach indynamictrajectorytracking.Publicationtype: Conference Paper83 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Topology optimization considering contact and stress constraintsThis work advances stress-constrained topology optimization for frictionless unilateral contact problems involving large deformations and hyperelastic material behavior. The objective is to minimize the mass of a contact-constrained structure while enforcing stress constraints through local or global stress measures. Different contact coupling techniques based on Lagrange multipliers and penalty methods are investigated with respect to their applicability in stress-constrained optimization. The results show that stress-constrained optimization is considerably more sensitive to discretization errors at curved contact surfaces than common objectives such as compliance. Such errors may introduce artificial stresses that mislead the optimizer towards poorly performing designs. To address this issue, blending functions are used to retain the physical contact geometry during finite-element-based optimization, thereby avoiding the computational cost of very fine contact-surface discretizations. Furthermore, node-to-segment and segment-to-segment contact coupling are compared directly in stress-constrained topology optimization. The smoother contact-pressure and stress distributions provided by segment-to-segment coupling improve the optimization performance and lead to lighter feasible designs. Finally, local and global stress constraint formulations are compared, and an extension of the augmented Lagrangian approach is proposed to address convergence issues in challenging contact scenarios.Publicationtype: Journal Article53 12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Combined feedforward and feedback dynamic trajectory tracking control of a soft robotSoft robots are an emerging and fast-growing field of research with applications in various areas. With the rise of new soft robotic applications, control requirements increase. Thus, control of dynamic motion is becoming more and more important. The main challenges in controlling soft robots stem from their large, continuous deformations, which lead to many degrees of freedom, as well as the difficulty of sensor integration, which leads to a limited availability of control feedback. Additionally, unknown material properties and comparatively large manufacturing inaccuracies hinder reliable modeling of soft robots. So far, in soft robotics mainly so-called quasi-static control approaches, which neglect the dynamics of the system, are used. However, modern soft robotic applications demand increasingly faster and more accurate movements. In these cases, quasi-static controllers are insufficient, and controllers which account for the robots dynamics are needed.In this work different feedforward and feedback control approaches for dynamic trajectory tracking control of soft robots are implemented and examined. A neural-network-based quasi-static feedforward controller, a dynamic feedforward controller based on the inversion of a dynamic piecewise constant curvature (PCC) model using servo-constraints, and the combination of the two different feedforward controllers with a linear quadratic regulator with integral action (LQRi) and gain scheduling are considered. For feedback control, only sensor feedback obtained from a curvature sensor integrated into the soft robot is used. The performance of these approaches is compared in simulations and experiments.Publicationtype: Conference Paper37 - Some of the metrics are blocked by yourconsent settings
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 , Dynamic open-loop trajectory tracking control of a soft robot using servo-constraintsPublicationtype: Conference Paper76 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , On the singularity and numerical instability of flow fields associated with the nonlinear Schrödinger equationThe nonlinear Schrödinger equation (NLS) is a well-known equation in the study of wave motion. In the context of nonlinear water waves, the NLS has been proven to accurately model deep-water waves with a narrow spectral bandwidth and moderate wave steepness. While many studies have focused on reconstructing free surface profiles using the NLS, the associated flow field within the fluid has received less attention. Recently, a paper was published in which the flow field associated with solutions of the NLS was constructed. Here, it has been noted that the associated modeled flow field can contain singularities below the water surface. This work analyzes the occurrence and location of such artificial singularities for different analytical NLS solutions. It is found that the flow field can generally become singular at any depth below the surface and may even appear periodically in water depth. Additionally, it is shown that the numerical computation of the flow field poses considerable challenges due to the instability of numerical schemes. These findings are crucial for the accurate analysis of flow fields corresponding to the NLS.Publicationtype: Journal Article53 13 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Analysis of the behavior of a linear axis with an industrial robot(Institut für Fertigungstechnik und Werkzeugmaschinen IFW, 2026-02); ; ; Industrial robots offer a variety of advantages for large-part manufacturing, e.g. the machining of vertical tail planes or wing spars, over conventional machine tools. Their compact footprint and modular design enable broad industrial applicability. However, the spherical workspace of a single articulated industrial robot may be insufficient for large parts. To overcome this limitation, industrial robots are combined with workspace extensions like linear axes. When the linear axis is interpolated with the robot joints, it enables continuous process movement, increasing production speed and flexibility. Mounting the robot on the linear axis introduces the carriage, a dynamic body with distinct properties, into the kinematic chain; consequently, as the first dynamic element, the carriage significantly influences the articulated robot and the combined system’s dynamics. Experiments on a linear axis designed for path-accurate robot processes including two rack-and-pinion drives demonstrate that the coupling effects of the linear axis and the articulated robot, as well as the dynamic properties of the combined system, are pose-dependent. The optimal integration of the external axis into the control system requires a thorough characterization of these dynamic properties. This work presents a rigid multibody model inspired by an inverse pendulum that couples the linear axis translatory with the mounted robots’ rotary axes movements at acceleration level and by trigonometric terms. The model reveals how dynamic properties of the linear axis change according to the robots pose and its multi-DOF stiffness and damping properties. This enables the optimization of control system parameters and the narrowing of filter bandwidths. An approach for industrial controller tuning based on the introduced model is presented. Implementation focuses on the linear axis within the industrial controller, with improvements validated through path measurement of the tool center point of the robot.Publicationtype: Conference Paper24 4 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A computational method for the fluid-structure interaction in nonlinear ocean waves using the nonlinear Schrödinger equationAn efficient computation of the fluid-structure interaction (FSI) between mechanical structures and ocean waves is an important topic for many applications in ocean engineering and naval architecture. For many of such applications, it is often sufficient to consider the FSI within the potential flow theory instead of using the Navier-Stokes equations. However, the numerical solution of the governing equations of the potential flow theory is still computationally expensive. This work contributes to the improvement of the currently available numerical schemes, which compute the FSI in nonlinear ocean waves. The approach used in this work is based on the nonlinear Schrödinger equation (NLS). Compared to the governing equations of potential flow theory, solutions of the NLS can be computed much more efficiently. This efficiency advantage is used to develop a method that allows a fast computation of the FSI in nonlinear water waves. The developed method is investigated with regard to its accuracy, application areas, and computational effort. Here, numerical results for the hydrodynamic forces acting on mechanical structures and the resulting motion of the structures are analyzed. It is found that the NLS offers an interesting possibility to efficiently investigate the behavior of structures excited by nonlinear ocean waves.Publicationtype: Journal Article80 30 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Modeling, sensing, and control for agile trajectory tracking of soft robots(2026); Accurate, agile trajectory tracking is important for modern soft robotic applications. This requires models that accurately represent dynamic behavior, soft sensors for state estimation, and dynamic controllers. In this work, first, different model-based and data-driven modeling approaches for soft robots are presented and evaluated in terms of accuracy and efficiency. Second, curvature sensors that can be integrated into the soft robot body to measure its configuration are presented and evaluated experimentally. Third, feedforward and feedback control approaches for soft robots are presented and evaluated in simulations and experiments.Publicationtype: Doctoral Thesis118 75 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Robust design optimization of structures with multimodal responses using Taylor expansions(Springer, 2025-11-15); ; ; ; This paper shows how a combination of multiple linear approximations can efficiently capture nonlinear and non-smooth response functions in the framework of robust design optimization. The basic idea is to split the domain of random parameters into multiple subdomains along discontinuities of the response. For each subdomain, a linear approximation is used to determine the stochastic moments of the response function due to the scatter in that subdomain. The total stochastic distribution of the structural response is considered to be a multimodal distribution, and its moments are composed of the approximations in the subdomains. The application to use cases shows that indeed the structural response is multimodal in these cases, and the stochastic moments are shown to be sufficiently accurate using the proposed method.Publicationtype: Journal Article65 46