@misc{RuiBestle, author = {Rui, Xue and Bestle, Dieter}, title = {Reduced Multibody System Transfer Matrix Method Using Decoupled Hinge Equations}, series = {International journal of mechanical system dynamics : IJMSD}, volume = {1}, journal = {International journal of mechanical system dynamics : IJMSD}, number = {2}, issn = {2767-1402}, doi = {10.1002/msd2.12026}, pages = {182 -- 193}, language = {en} } @misc{ZhouFehrBestleetal., author = {Zhou, Qinbo and Fehr, J{\"o}rg and Bestle, Dieter and Rui, Xiaoting}, title = {Simulation of Elastic Body Dynamics with Large Motion Using Transfer Matrix Method}, series = {Multibody System Dynamics}, volume = {59 (2023)}, journal = {Multibody System Dynamics}, number = {3}, issn = {1573-272X}, doi = {10.1007/s11044-022-09869-2}, pages = {269 -- 292}, language = {en} } @misc{dePayrebruneFlasskampStroehlaetal., author = {de Payrebrune, Kristin and Flaßkamp, Kathrin and Str{\"o}hla, Tom and Sattel, Thomas and Bestle, Dieter and al, et}, title = {The impact of Al on engineering design procedures for dynamical systems}, series = {Electrical Engineering and Systems Science. Systems and Control}, journal = {Electrical Engineering and Systems Science. Systems and Control}, publisher = {Cornell University}, doi = {10.48550/arXiv.2412.12230}, pages = {23}, language = {en} } @misc{ZhaiBestle, author = {Zhai, Tianyu and Bestle, Dieter}, title = {Design of two coupled fuzzy controllers for a planar direct internat reforming solid oxide fuel cell}, series = {Proceedings in Applied Mathematics and Mechanics}, volume = {24}, journal = {Proceedings in Applied Mathematics and Mechanics}, number = {4}, publisher = {Wiley-VCH GmbH}, doi = {10.1002/pamm.202400077}, language = {en} } @incollection{Bestle, author = {Bestle, Dieter}, title = {Optimization processes for automated design of industrial systems}, series = {Optimal design and control of multibody systems : proceedings of the IUTAM symposium}, booktitle = {Optimal design and control of multibody systems : proceedings of the IUTAM symposium}, editor = {Nachbagauer, Karin and Held, Alexander}, publisher = {Springer}, address = {Hamburg}, isbn = {978-3-031-49999-9}, issn = {1875-3507}, doi = {10.1007/978-3-031-50000-8_1}, pages = {3 -- 15}, language = {en} } @misc{KayatasBestle, author = {Kayatas, Zafer and Bestle, Dieter}, title = {Scenario identification and classification to support the assessment of advanced driver assistance systems}, series = {Applied Mechanics}, volume = {5}, journal = {Applied Mechanics}, number = {3}, publisher = {MDPI}, issn = {2673-3161}, doi = {10.3390/applmech5030032}, pages = {563 -- 578}, abstract = {In recent years, driver assistance systems in cars, buses, and trucks have become more common and powerful. In particular, the introduction of AI methods to sensors, signal fusion, and traffic recognition allows us to step forward from actual level-2 assistance to level-3 A dvanced D river A ssistance S ystems (ADAS), where driving becomes autonomous and responsibility shifts from the driver to the automobile manufacturers. This, however, requires a high-precision risk assessment of failure, which can only be achieved by extensive data acquisition and statistical analysis of real traffic scenarios (which is impossible to perform by humans). Therefore, critical driving situations have to be identified and classified automatically. This paper develops and compares two different strategies—a traditional rule-based approach derived from deterministic causal considerations, and an AI-based approach trained with idealized cut-in, cut-out, and cut-through maneuvers. Application to a 10-h measurement sequence on a German highway demonstrates that the latter has the higher performance, whereas the former misses some of the safety-relevant events to be identified.}, language = {en} } @misc{BielitzBestle, author = {Bielitz, Timo and Bestle, Dieter}, title = {Artificial recurrent model for parameter identification of dynamic systems}, series = {Proceedings in Applied Mathematics \& Mechanics}, journal = {Proceedings in Applied Mathematics \& Mechanics}, publisher = {Wiley-VCH GmbH}, issn = {1617-7061}, doi = {10.1002/pamm.202400015}, pages = {9}, language = {en} } @misc{BestleBielitz, author = {Bestle, Dieter and Bielitz, Timo}, title = {Real-time models for systems with costly or unknown dynamics}, series = {Special Issue: 94th Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM)}, volume = {24}, journal = {Special Issue: 94th Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM)}, number = {2}, issn = {1617-7061}, doi = {10.1002/pamm.202400008}, pages = {8}, abstract = {Many applications require real-time simulation where the computer model can be executed at least as fast as the underlying physical system. However, as the complexity of models grows, for example, in case of flexible multibody dynamics accounting for body elasticity, this becomes harder even with high-end computers and parallel computing. Further the modeling itself could be too cumbersome to come up with causal models, especially in industrial applications, or modeling is inhibited by unknown physical effects. In such cases, data-based modeling with AI-strategies may be an alternative to come up with real-time capable simulation models. Based on measured or costly simulated trajectories, state function values of the dynamic system are first identified from divided differences of subsequent states, and then used for training a feedforward neural network. This can then be used as an approximate surrogate for further simulations or as part of a real-time application. Applications to the Duffing equation and a closed-loop mechanism demonstrate high conformity of predicted and directly simulated trajectories reproducing even the bifurcation behavior of the Duffing equation.}, language = {en} } @misc{BielitzBestle, author = {Bielitz, Timo and Bestle, Dieter}, title = {Identification of dynamic systems assisted by an autoregressive recurrent model}, series = {Proceedings in Applied Mathematics \& Mechanics : PAMM}, volume = {23}, journal = {Proceedings in Applied Mathematics \& Mechanics : PAMM}, number = {2}, issn = {1617-7061}, doi = {10.1002/pamm.202300086}, pages = {8}, abstract = {The identification of parameters in dynamic systems usually first requires modeling the system as nonlinear differential equations based on physical principles, which then can be evaluated to search for a set of optimal parameters that enable the mathematical model to reproduce some desired behavior of the real system. To overcome the necessity of complex modeling and to possibly reduce the number of extensive experimental evaluations of the real system or numerical evaluations of the differential equations, strategies from machine learning may be applied. The proposed method uses recurrent architectures such as Long Short-Term Memory (LSTM) networks as core to predict the system development from initial conditions and parameter values. The trainable weights of the model are optimized based on a set of training data containing parameter values and corresponding solution trajectories generated by evaluation of the system to be investigated. The trained model may then be used to identify unknown system parameter values related to a specific solution trajectory by solving an optimization problem for the inputs of the machine learning model.}, language = {en} } @misc{NiehoffBestleKupijai, author = {Niehoff, Malte and Bestle, Dieter and Kupijai, Philipp}, title = {Model-based design optimization taking into account design viability via classification}, series = {Engineering Modelling, Analysis and Simulation : the NAFEMS Journal}, volume = {1}, journal = {Engineering Modelling, Analysis and Simulation : the NAFEMS Journal}, number = {1}, publisher = {NAFEMS Ltd.}, address = {Knutsford}, issn = {3005-9763}, doi = {10.59972/c7b5hzx7}, pages = {1 -- 12}, language = {en} } @misc{MammenKayatasBestle, author = {Mammen, Manasa Mariam and Kayatas, Zafer and Bestle, Dieter}, title = {Evaluation of different generative models to support the validation of advanced driver assistance systems}, series = {Applied mechanics}, volume = {6}, journal = {Applied mechanics}, number = {2}, publisher = {MDPI}, address = {Basel}, issn = {2673-3161}, doi = {10.3390/applmech6020039}, pages = {1 -- 26}, abstract = {Validating the safety and reliability of automated driving systems is a critical challenge in the development of autonomous driving technology. Such systems must reliably replicate human driving behavior across scenarios of varying complexity and criticality. Ensuring this level of accuracy necessitates robust testing methodologies that can systematically assess performance under various driving conditions. Scenario-based testing addresses this challenge by recreating safety-critical situations at varying levels of abstraction, from simulations to real-world field tests. However, conventional parameterized models for scenario generation are often resource intensive, prone to bias from simplifications, and limited in capturing realistic vehicle trajectories. To overcome these limitations, the paper explores AI-based methods for scenario generation, with a focus on the cut-in maneuver. Four different approaches are trained and compared: Variational Autoencoder enhanced with a convolutional neural network (VAE), a basic Generative Adversarial Network (GAN), Wasserstein GAN (WGAN), and Time-Series GAN (TimeGAN). Their performance is assessed with respect to their ability to generate realistic and diverse trajectories for the cut-in scenario using qualitative analysis, quantitative metrics, and statistical analysis. Among the investigated approaches, VAE demonstrates superior performance, effectively generating realistic and diverse scenarios while maintaining computational efficiency.}, language = {en} } @misc{RoederHajipourEbeletal., author = {R{\"o}der, Benedict and Hajipour, Sanam and Ebel, Henrik and Eberhard, Peter and Bestle, Dieter}, title = {Automated design of a four-bar mechanism starting from hand drawings of desired coupler trajectories and velocity profiles}, series = {Mechanics based design of structures and machines}, volume = {2025}, journal = {Mechanics based design of structures and machines}, publisher = {Taylor \& Francis}, address = {Philadelphia, Pa.}, issn = {1539-7742}, doi = {10.1080/15397734.2025.2543559}, pages = {1 -- 25}, abstract = {When a specific, relatively simple motion must be executed with high precision and high speed, simple mechanisms like the four-bar linkage are often the first choice. Combined with control of the crank speed, these systems are able to move the end-effector with any changing speed. Designing a certain mechanism for a pre-defined trajectory, however, usually requires an expert, which is why automatic design by a design assistant system is desirable. Modern AI methods may support such an automatic design, which shall function only based on hand drawings of the desired path and a corresponding velocity profile. For a planar four-bar mechanism, which serves only as an example, this article proposes a general data-driven procedure where first the geometry of a mechanism is synthesized by machine learning such that the track point follows approximately the desired trajectory. Then, the kinematic relation between the track point's velocity profile and the crank's angular velocity is learned. This learned inverse kinematics model may be applied to an arbitrary user-defined velocity profile of the track point. Finally, a controller governing the crank torque is designed purely based on data to track the resulting angular crank velocity. Not every drawn motion can necessarily be realized with a mechanism; however, the goal is to identify a mechanism that approximates the drawn curve as accurately as possible. While various design toolboxes already exist, especially for the design of four-bar linkages, this article presents a design pipeline using data-driven approaches that solely learn from forward simulation data. This nurtures the hope that presented approach is also applicable to more complex systems where analytic formulations cannot be easily derived.}, language = {en} } @misc{HenkelBestleFlassigetal., author = {Henkel, Clara and Bestle, Dieter and Flassig, Peter and Janke, Christian and Slaby, Michael}, title = {Consideration of balancing within the fan-blisk design process}, series = {GPPS Shanghai25 Technical Paper Proceedings}, journal = {GPPS Shanghai25 Technical Paper Proceedings}, publisher = {Global Power and Propulsion Society (GPPS)}, address = {Zug, Schweiz}, doi = {10.33737/gpps25-tc-017}, pages = {1 -- 9}, abstract = {The design of fan-blisks is a multi-criterion optimisation challenge primarily involving the aerodynamic shape optimisation of blade profiles and subsequent blade balancing, i.e., shifting profiles in axial and circumferential directions to avoid stress hotspots. Although it is already known that blade balancing affects aerodynamic properties, this correlation is often not taken into account, which is why it is usually performed as an independent step after solving the aerodynamic design problem. However, this assumption is questionable because such shifts should be used to control both secondary flow effects and resulting stress. Therefore, the paper proposes problem formulations combining both aspects. Since optimisation requires costly numerical evaluations, machine learning methods are investigated to predict aerodynamic performance and stress constraints more efficiently. This enables a global optimisation process by reducing computational costs. Various different surrogate types are investigated, where stress constraints are formulated either as regression task predicting stress maxima, or as classification problem directly assessing design feasibility.}, language = {en} } @misc{BestleHajipour, author = {Bestle, Dieter and Hajipour, Sanam}, title = {Design of a tracking controller based on machine learning}, series = {International journal of mechanical system dynamics}, volume = {5 / 2025}, journal = {International journal of mechanical system dynamics}, number = {2}, publisher = {Wiley}, address = {Hoboken, NJ}, issn = {2767-1402}, doi = {10.1002/msd2.70006}, pages = {201 -- 211}, abstract = {Tracking control of multibody systems is a challenging task requiring detailed modeling and control expertise. Especially in the case of closed-loop mechanisms, inverse kinematics as part of the controller may become a game stopper due to the extensive calculations required for solving nonlinear equations and inverting complicated functions. The procedure introduced in this paper substitutes such advanced human expertise by artificial intelligence through the utilization of surrogates, which may be trained from data obtained by classical simulation. The necessary steps are demonstrated along a parallel mechanism called λ-robot. Based on its mechanical model, the workspace is investigated, which is required to set proper initial conditions for generating data covering the used operation space of the robot. Based on these data, artificial neural networks are trained as surrogates for inverse kinematics and inverse dynamics. They provide forward control information such that the remaining error behavior is governed by a linear ordinary differential equation, which allows applying a linear quadratic regulator (LQR) from linear control theory. An additional feedback loop of the tracking error accounts for model uncertainties. Simulation results validate the applicability of the proposed concept.}, language = {en} } @misc{MammenKayatasBestle, author = {Mammen, Manasa Mariam and Kayatas, Zafer and Bestle, Dieter}, title = {Generation of multiple types of driving scenarios with variational autoencoders for autonomous driving}, series = {Future transportation}, volume = {5}, journal = {Future transportation}, number = {4}, publisher = {MDPI}, address = {Basel}, issn = {673-7590}, doi = {10.3390/futuretransp5040159}, pages = {1 -- 20}, abstract = {Generating realistic and diverse driving scenarios is essential for effective scenario-based testing and validation in autonomous driving and the development of driver assistance systems. Traditionally, parametric models are used as standard approaches for scenario generation, but they require detailed domain expertise, suffer from scalability issues, and often introduce biases due to idealizations. Recent research has demonstrated that AI models can generate more realistic driving scenarios with reduced manual effort. However, these models typically focused on single scenario types, such as cut-in maneuvers, which limits their applicability to diverse real-world driving situations. This paper, therefore, proposes a unified generative framework that can simultaneously generate multiple types of driving scenarios, including cut-in, cut-out, and cut-through maneuvers from both directions, thus covering six distinct driving behaviors. The model not only learns to generate realistic trajectories but also reflects the same statistical properties as observed in real-world data, which is essential for risk assessment. Comprehensive evaluations, including quantitative metrics and visualizations from detailed latent and physical space analyses, demonstrate that the unified model achieves comparable performance to individually trained models. The shown approach reduces modeling complexity and offers a scalable solution for generating diverse, safety-relevant driving scenarios, supporting robust testing and validation.}, language = {en} } @misc{ZhaiBestle, author = {Zhai, Tianyu and Bestle, Dieter}, title = {Design of nonlinear model predictive control for planar direct internal reforming solid oxide fuel cell stacks}, series = {2025 44th Chinese Control Conference (CCC)}, journal = {2025 44th Chinese Control Conference (CCC)}, publisher = {IEEE}, address = {Piscataway, NJ}, isbn = {978-988-75816-1-1}, doi = {10.23919/CCC64809.2025.11179672}, pages = {3093 -- 3098}, abstract = {The paper proposes an optimization-based nonlinear model predictive control (NMPC) strategy for the operation of a planar direct internal reforming solid oxide fuel cell (DIR-SOFC). The strategy focuses on regulating the inlet fuel and air flowrates to meet power demands, maintain the design temperature, and adhere to other operational constraints. A state estimator and optimizer are employed. Control and prediction horizons, as well as the time step lengths are carefully selected to balance estimation accuracy, stability, and computational cost. The obtained results demonstrate the effectiveness of NMPC in managing large-scale energy supply systems. Comparative analyses with varying tuning weights demonstrate the importance of appropriate weight selection regarding control performance and the robustness of NMPC. Both the dynamic modeling of the planar DIR-SOFC and the NMPC framework are implemented in the MATLAB/Simulink environment.}, language = {en} } @misc{HajipourZhaiBestle, author = {Hajipour, Sanam and Zhai, Tianyu and Bestle, Dieter}, title = {Data-driven sliding mode control of a rarallel 2D-robot}, series = {2025 8th International Conference on Intelligent Robotics and Control Engineering (IRCE)}, journal = {2025 8th International Conference on Intelligent Robotics and Control Engineering (IRCE)}, publisher = {IEEE}, address = {Piscataway, NJ}, isbn = {979-8-3503-5710-3}, doi = {10.1109/irce66030.2025.11203089}, pages = {75 -- 80}, abstract = {The paper introduces an easy-to-implement control strategy for a parallel 2 D robot. To avoid any analytic computations of multibody system dynamics and kinematics of the closed-loop mechanism, two feedforward networks are utilized as surrogates for inverse models. The necessary data are generated using classical simulation starting from random initial conditions and applying random force to cover the full state space used during control. Closed-loop stability of the lambda robot is guaranteed by extending the forward control based on inverse dynamics by a sliding mode controller with super-twisting structure. This approach results in an error decay to very low magnitude orders as an investigation of the tracking performance demonstrating high efficiency and control accuracy even when the plant parameters are disturbed.}, language = {en} } @misc{HajipourBestle, author = {Hajipour, Sanam and Bestle, Dieter}, title = {Surrogate-based robust tracking controller for a Lambda robot}, series = {Applied mathematics and mechanics : special issue : 95th Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM)}, volume = {25}, journal = {Applied mathematics and mechanics : special issue : 95th Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM)}, number = {3}, publisher = {Wiley-VCH}, address = {Weinheim}, doi = {10.1002/pamm.70017}, pages = {1 -- 8}, abstract = {Tracking control of multibody systems typically requires precise inversion of complex kinematic and dynamic equations, which can be particularly challenging for closed-loop mechanisms. This study presents a surrogate-based control approach that leverages artificial neural networks to approximate inverse kinematics and dynamics using data generated from classical simulations of a nominal model. These data-driven surrogates enable feedforward prediction of joint trajectories and control inputs without requiring analytical model inversion. To ensure robustness under modeling errors and parametric uncertainties, additional inner- and outer-loop sliding mode control (SMC) loops are integrated. Simulation results from a lambda robot demonstrate high tracking accuracy with asymptotic steady-state errors even under perturbations of system parameters. The proposed framework establishes a structured approach for trajectory tracking by combining data-driven feedforward and robust feedback control suitable for complex nonlinear robotic systems.}, language = {en} }