Control-relevant nonlinearity measure and integrated multi-model control. (September 2017)

• Record Type: Journal Article
• Authors: Du, Jingjing; Johansen, Tor Arne
• Is Part Of: Journal of process control. Volume 57(2017)
• Page Start: 127
• Page End: 139
• Publication Date: 2017-09
• Subjects: Control-relevant nonlinearity measure -- Gap metric -- Weighting method -- Integrated multi-model control -- CSTR; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2017.07.001
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: A control-relevant nonlinearity measure (CRNM) method is proposed for chemical processes. Supported by the CRNM method, an integrated multi-model control framework is developed. Dependency on a priori knowledge and computational load are reduced. A 1/ δ gap-based weighting method is put forward to combine the local controllers. The 1/ δ weighting method is sensitive to the tuning parameter, flexible and easy to tune. Abstract: A control-relevant nonlinearity measure (CRNM) method is proposed based on the gap metric and the gap metric stability margin to measure the nonlinear degree of a system once a linear control strategy is selected. Supported by the CRNM method, an integrated multi-model control framework is developed, in which the multi-model decomposition and local controller design are closely integrated, model redundancy is avoided, computational load is reduced, and dependency on a prior knowledge is reduced. Besides, a 1/ δ gap-based weighting method is put forward to combine the local controllers. On one hand, the 1/ δ gap-based weighting method has merely one tuning parameter and can be computed off-line; on the other hand, it is sensitive to the tuning parameter, flexible and easy to tune. Two continuous stirred tank reactor (CSTR) systems are investigated. Closed-loop simulations validate the effectiveness and benefits of the proposed integrated multi-model control approach based on CRNM.