A Numerical Study of the Robustness of Transcription Methods for Parameter Identification Problems

Abstract

In this work, we apply transcription methods known from optimal control to nonlinear dynamic parameter identification problems. We analyze and compare the methods with the help of an application example from robotics. More specifically, we investigate their robustness against varying initial parameters and show numerically, that an increasing number of variables leads to a more robust optimization behavior and qualitatively better solutions.