A frequency domain approach for parameter identification in multibody dynamics

Oberpeilsteiner, Stefan; Lauß, Thomas; Steiner, Wolfgang; Nachbagauer, Karin

doi:10.1007/s11044-017-9596-1

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…Reference 20 combines least squares techniques and Kalman filters, while the proposed algorithm identifies a minimum order plant based on input–output data. A parametric identification for multibody dynamics using the adjoint method was illustrated in References 21,22. Others relevant works in the topic can be found in References 23‐29 and a survey on the parameter identification methods for robots in Reference 30.…”

Section: Introductionmentioning

confidence: 99%

A non‐inherent parametric estimation for dynamical equivalence of flexible manipulators

Bastos

2022

Optim Control Appl Methods

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This study aims to develop a methodology for parametric estimation of flexible manipulators considering an equivalent underactuated manipulator with passive joints, which should allow better possibilities to control flexible systems when addressing challenging problems to reach a satisfactory tracking motion. Theoretically, this is achieved by a minimum number of internal dynamics variables needed for representing almost all dynamics of the system. The parametric estimation approach is based on the optimal control theory considering a robust, stable method. This methodology requires only measured/computed data that are system output. Validation occurs through a virtual plant of the flexible manipulator observing a numerical perspective. The systems studied have serial and parallel kinematics and are mounted on carts for more diverse applications and general possibilities of the methodology.

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Section: Introductionmentioning

confidence: 99%

A non‐inherent parametric estimation for dynamical equivalence of flexible manipulators

Bastos

2022

Optim Control Appl Methods

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“…To overcome the problem of convergence to a local minimum, homotopy transformation motivated by the theory of state observers was proposed [9]. The adjoint method, specifically the Fourier coefficients, was employed as an approach for identifying the amplitude response of oscillations [10]. Markov parameters obtained through a state observer and a least-square approach were employed for the procedure, following which a state-space model of the mechanical system using generalized Hankel matrices were constructed [11].…”

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confidence: 99%

SID-Net: Machine learning based system identificationframework for rigid and flexible multibody dynamics

Jang,

Han,

Kim

et al. 2023

Preprint

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Modeling and simulation of dynamic systems is widely used in mechanical system design and control. System identification (SI), a process of correlation using experimental or target data, is essential for the reliability of implemented numerical models. To actualize the process, it is crucial to understand the relationship between numerous modeling parameters that affect the system responses. Modeling and simulating nonlinear systems such as multibody dynamics, is particularly difficult owing to their characteristic enormous assumptions and approximations; furthermore, the computational demands are also crucial during the iterative SI process. Therefore, we propose an effective SI framework for rigid and flexible multibody dynamics using artificial neural networks. The framework consists of two phases: a system metamodel and a SI network (SIN). Through supervised learning, a metamodel that can provide nonlinear time transient responses based on the input system design parameters in real time was generated. The loss minimization process for initializing a new neural network that, utilizes a SIN to find the input design parameters that generate the desired target output was also introduced. A new loss function was defined for minimizing the difference between the output of the trained system metamodel, following which the target output was defined. The performance of the proposed framework was evaluated in well-designed numerical and experimental examples.

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