Title
A Novel Method for Control Performance Assessment with Fractional Order Signal Processing and Its Application to Semiconductor Manufacturing.
Abstract
The significant task for control performance assessment (CPA) is to review and evaluate the performance of the control system. The control system in the semiconductor industry exhibits a complex dynamic behavior, which is hard to analyze. This paper investigates the interesting crossover properties of Hurst exponent estimations and proposes a novel method for feature extraction of the nonlinear multi-input multi-output (MIMO) systems. At first, coupled data from real industry are analyzed by multifractal detrended fluctuation analysis (MFDFA) and the resultant multifractal spectrum is obtained. Secondly, the crossover points with spline fit in the scale-law curve are located and then employed to segment the entire scale-law curve into several different scaling regions, in which a single Hurst exponent can be estimated. Thirdly, to further ascertain the origin of the multifractality of control signals, the generalized Hurst exponents of the original series are compared with shuffled data. At last, non-Gaussian statistical properties, multifractal properties and Hurst exponents of the process control variables are derived and compared with different sets of tuning parameters. The results have shown that CPA of the MIMO system can be better employed with the help of fractional order signal processing (FOSP).
Year
DOI
Venue
2018
10.3390/a11070090
ALGORITHMS
Keywords
Field
DocType
control performance assessment,fractional order signal processing (FOSP),multifractal detrended fluctuation analysis (MFDFA),semiconductor manufacturing,Hurst exponent
Spline (mathematics),Signal processing,Mathematical optimization,Nonlinear system,Crossover,Hurst exponent,Algorithm,Detrended fluctuation analysis,Control system,Multifractal system,Mathematics
Journal
Volume
Issue
ISSN
11
7
1999-4893
Citations 
PageRank 
References 
1
0.41
3
Authors
4
Name
Order
Citations
PageRank
Kai Liu121.12
Yangquan Chen22257242.16
Pawel D. Domanski345.64
Xi Zhang410.75