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[7/8]Data-driven Multivariable Control Performance Monitoring

TopicData-driven Multivariable Control Performance Monitoring

ReporterDr. S. Joe Qin

Place:Founder 608

Automatic control is realized by means of feedback of real time information to adjust a set of manipulated variables continuously. Process monitoring provides supervision of process operations so that abnormal operating conditions can be detected, diagnosed, and proper adjustment can be implemented as needed. The common purpose is to reduce process variability under real-world operating conditions with the use of real time data.
    In this seminar, a sample-covariance benchmark is proposed for control performance monitoring. Within the covariance monitoring scheme, generalized eigenvalue analysis is used to extract the directions with the degraded or improved control performance against the benchmark. A statistical inference method is further developed for the generalized eigenvalues and the corresponding confidence intervals are derived from asymptotic statistics. This procedure can be used to determine the directions or subspaces with significantly worse or better performance versus the benchmark. The covariance-based performance indices within the isolated worse and better performance subspaces are then derived to assess the performance degradation and improvement.


Dr. S. Joe Qin is the Fluor Professor at the Viterbi School of Engineering at University of Southern California and Chang Jiang Professor affiliated with Tsinghua University by the Ministry of Education of China. Prior to joining USC he held the Paul D. and Betty Robertson Meek and American Petrofina Foundation Centennial Professorship in Chemical Engineering at University of Texas at Austin. He obtained his B.S. and M.S. degrees in Automatic Control from Tsinghua University in Beijing, China, in 1984 and 1987, respectively, and his Ph.D. degree in Chemical Engineering from University of Maryland at College Park in 1992. He was a Principal Engineer at Fisher-Rosemount from 1992 to 1995.
    Dr. Qin’s research interests include statistical process monitoring and fault diagnosis, model predictive control, system identification, run-to-run control, semiconductor process control, and control performance monitoring. He is a Co-Director of the Texas-Wisconsin-California Control Consortium where he has been principal investigator for 16 years. He is a recipient of the National Science Foundation CAREER Award, DuPont Young Professor Award, Halliburton/Brown & Root Young Faculty Excellence Award, NSF-China Outstanding Young Investigator Award, and an IFAC Best Paper Prize for the model predictive control survey paper published in Control Engineering Practice. He is currently an Associate Editor for Journal of Process Control and IEEE Transactions on Industrial Informatics, and a Member of the Editorial Board for Journal of Chemometrics. He served as an Editor for Control Engineering Practice and an Associate Editor for IEEE Transactions on Control Systems Technology. He is a Fellow of IEEE.