Data Quantisation and Closed-Loop System Identification. Issue 28 (2015)

• Record Type: Journal Article
• Authors: Shardt, Yuri A.W.; Ding, Steven X.
• Is Part Of: IFAC-PapersOnLine. Volume 48:Issue 28(2015)
• Page Start: 128
• Page End: 133
• Publication Date: 2015
• Subjects: system identification -- data quantisation -- process monitoring; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2015.12.112
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: In the development of a rigorous and complete procedure for automating the performance of data-driven process identification, there is a need to consider data quantisation. Such an issue can arise when the sensors have not been properly calibrated for the range of values experienced in the actual process. Through a detailed mathematical analysis of the problem, it is shown that the ratio between the variance of the signal and the gap between quantisation levels strongly influences the ability to identify a process. Using this criterion, a data quantisation index is proposed that allows for the effect of data quantisation on the data system to be quantified. Monte Carlo simulations of a closed-loop system with different system properties is examined to show that the proposed index can accurately distinguish between good and bad data quantisation.