scholarly journals Enhancement of the Adjoint Method by Error Control of Accelerations for Parameter Identification in Multibody Dynamics

2015 ◽  
Vol 3 (3) ◽  
pp. 47-52 ◽  
Author(s):  
Karin Nachbagauer ◽  
Stefan Oberpeilsteiner ◽  
Wolfgang Steiner
Keyword(s):  
Parameter Identification ◽  
Multibody Dynamics ◽  
Error Control ◽  
Adjoint Method

The Use of the Adjoint Method for Solving Typical Optimization Problems in Multibody Dynamics

2015 ◽  
Vol 10 (6) ◽  
Author(s):  
Karin Nachbagauer ◽  
Stefan Oberpeilsteiner ◽  
Karim Sherif ◽  
Wolfgang Steiner
Keyword(s):  
Optimal Control ◽  
Parameter Identification ◽  
Multibody Dynamics ◽  
Trajectory Tracking ◽  
Adjoint Method ◽  
Inverse Dynamics ◽  
Optimization Problems ◽  
Complicated Structure ◽  
Wide Range ◽  
Classical Optimization

The present paper illustrates the potential of the adjoint method for a wide range of optimization problems in multibody dynamics such as inverse dynamics and parameter identification. Although the equations and matrices included show a complicated structure, the additional effort when combining the standard forward solver to the adjoint backward solver is kept in limits. Therefore, the adjoint method shows an efficient way to incorporate inverse dynamics to engineering multibody applications, e.g., trajectory tracking or parameter identification in the field of robotics. The present paper studies examples for both, parameter identification and optimal control, and shows the potential of the adjoint method in solving classical optimization problems in multibody dynamics.

scholarly journals PARAMETER IDENTIFICATION VIA THE ADJOINT METHOD: APPLICATION TO PROTEIN REGULATORY NETWORKS

2006 ◽  
Vol 39 (2) ◽  
pp. 475-482 ◽  
Author(s):  
Robin L. Raffard ◽  
Keith Amonlirdviman ◽  
Jeffrey D. Axelrod ◽  
Claire J. Tomlin
Keyword(s):  
Parameter Identification ◽  
Regulatory Networks ◽  
Adjoint Method

scholarly journals Adjoint accuracy for the full Stokes ice flow model: limits to the transmission of basal friction variability to the surface

2014 ◽  
Vol 8 (2) ◽  
pp. 721-741 ◽  
Author(s):  
N. Martin ◽  
J. Monnier
Keyword(s):  
Friction Coefficient ◽  
Parameter Identification ◽  
Adjoint Method ◽  
Synthetic Data ◽  
Adjoint Problem ◽  
Second Order ◽  
The Self ◽  
Algorithmic Differentiation ◽  
Numerical Assessment ◽  
Linear Friction

Abstract. This work focuses on the numerical assessment of the accuracy of an adjoint-based gradient in the perspective of variational data assimilation and parameter identification in glaciology. Using noisy synthetic data, we quantify the ability to identify the friction coefficient for such methods with a non-linear friction law. The exact adjoint problem is solved, based on second-order numerical schemes, and a comparison with the so-called "self-adjoint" approximation, neglecting the viscosity dependence on the velocity (leading to an incorrect gradient), common in glaciology, is carried out. For data with a noise of 1%, a lower bound of identifiable wavelengths of 10 ice thicknesses in the friction coefficient is established, when using the exact adjoint method, while the "self-adjoint" method is limited, even for lower noise, to a minimum of 20 ice thickness wavelengths. The second-order exact gradient method therefore provides robustness and reliability for the parameter identification process. In another respect, the derivation of the adjoint model using algorithmic differentiation leads to the formulation of a generalization of the "self-adjoint" approximation towards an incomplete adjoint method, adjustable in precision and computational burden.

scholarly journals A frequency domain approach for parameter identification in multibody dynamics

2017 ◽  
Vol 43 (2) ◽  
pp. 175-191
Author(s):  
Stefan Oberpeilsteiner ◽  
Thomas Lauss ◽  
Wolfgang Steiner ◽  
Karin Nachbagauer
Keyword(s):  
Parameter Identification ◽  
Frequency Domain ◽  
Multibody Dynamics ◽  
Frequency Domain Approach
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