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 for system identification 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 ( $9.28\pm0.87 \text{N } \text{vs. } 6.96\pm0.32 \text{N}$ of total model errors). This is significant, as it shows that the same data set can be used to identify unique subsystems, as opposed 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.

Keywords

• subsystem identification
• Gaussian process regression
• probabilistic machine learning