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

Abstract

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.