Global System Errors to Simultaneously Improve the Identification of Subsystems with Mixed Data Gaussian Process Regression

Abstract

This paper explores the use of Gaussian Process Regression (GPR) for system iden- tification in control engineering. It introduces two novel approaches that utilize the data from a measured global system error. The paper demonstrates these approaches by identifying a simulated system with three subsystems, a one degree of freedom mass with two antagonist muscles. The first approach uses this whole-system error data alone, achieving accuracy on the same order of magnitude as subsystem-specific data (9.28 ± 0.87 N vs. 6.96 ± 0.32 N of total model errors). This is significant, as it shows that the same data set can be used to identify unique subsystems, as op- posed to requiring a set of data descriptive of only a single subsystem. The second approach demonstrated in this paper mixes traditional subsystem-specific data with the whole system error data, achieving up to 98.71% model improvement.