TY - BOOK

T1 - Parameter Identification in Multibody System Dynamics using the Adjoint Sensitivity Analysis

AU - Oberpeilsteiner, Stefan

PY - 2018

Y1 - 2018

N2 - The usage of state-of-the-art software for analyzing the dynamics of multibody
systems allows to reduce the number of prototypes in an product development
process. Often, unknown parameters cause notable deviations of simulation results
compared to measurements taken during experiments. Improving the quality of the
virtual prototype may be achieved by matching the parameters used in the simulation
model with the real ones. With an increasing number of parameters, adjusting
their values manually in order to improve the accordance is hardly possible due to
the complexity of the multibody system. Therefore, an automated and efficient
strategy for parameter identification represents the only reasonable approach for
gaining better simulation results. Another problem arises due to the fact, that the
chosen excitations may not cause a sufficient reaction of the components under
consideration. In such a case the result of the identification relies on insufficient
data, and therefore the accuracy of the virtual prototype is not satisfactory.
Automating the process of parameter identification requires a meaningful performance
measure in order to quantify the deviation of experiment and simulation.
This allows for using an iterative approach that aims at solving the optimization
problem that minimizes a scalar performance measure. The gradient required by
the optimization algorithm is computed by using the adjoint sensitivity analysis.
Addressing the problem raised by insufficient information contained in the measurements
is done by adjusting the system inputs in order to maximize the performance
measure’s sensitivity onto parameter changes, usually denoted as optimal input
design.
For both special issues, the computation of the gradient and the optimization of
system inputs, detailed derivations are done. Besides the description of procedures
developed, comprehensible examples are presented for emphasizing the performance
of the respective method.

AB - The usage of state-of-the-art software for analyzing the dynamics of multibody
systems allows to reduce the number of prototypes in an product development
process. Often, unknown parameters cause notable deviations of simulation results
compared to measurements taken during experiments. Improving the quality of the
virtual prototype may be achieved by matching the parameters used in the simulation
model with the real ones. With an increasing number of parameters, adjusting
their values manually in order to improve the accordance is hardly possible due to
the complexity of the multibody system. Therefore, an automated and efficient
strategy for parameter identification represents the only reasonable approach for
gaining better simulation results. Another problem arises due to the fact, that the
chosen excitations may not cause a sufficient reaction of the components under
consideration. In such a case the result of the identification relies on insufficient
data, and therefore the accuracy of the virtual prototype is not satisfactory.
Automating the process of parameter identification requires a meaningful performance
measure in order to quantify the deviation of experiment and simulation.
This allows for using an iterative approach that aims at solving the optimization
problem that minimizes a scalar performance measure. The gradient required by
the optimization algorithm is computed by using the adjoint sensitivity analysis.
Addressing the problem raised by insufficient information contained in the measurements
is done by adjusting the system inputs in order to maximize the performance
measure’s sensitivity onto parameter changes, usually denoted as optimal input
design.
For both special issues, the computation of the gradient and the optimization of
system inputs, detailed derivations are done. Besides the description of procedures
developed, comprehensible examples are presented for emphasizing the performance
of the respective method.

M3 - Doctoral Thesis

ER -