Closed-loop identification of plant and disturbance models based on data-driven generalized minimum variance regulatory control. (July 2022)

• Record Type: Journal Article
• Authors: Uematsu, Ryota; Masuda, Shiro; Kano, Manabu
• Is Part Of: Journal of process control. Volume 115(2022)
• Page Start: 197
• Page End: 208
• Publication Date: 2022-07
• Subjects: Closed-loop identification -- Generalized minimum variance -- Data-driven controller design; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2022.05.002
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: The paper addresses a closed-loop identification method based on generalized minimum variance (GMV) evaluation. Since the proposed method uses routine operation data, it requires no extra experiment with an external excitation signal. The model parameters of the plant and the disturbance are obtained simultaneously using a single set of input–output data generated by stochastic disturbance. A new variance criterion for closed-loop identification is derived through the conversion of the GMV evaluation function that has originally been developed for data-driven regulatory control. In the conversion, the feedback invariant polynomial, which is estimated from time series analysis of the closed-output signal, plays a key role. The features of the proposed approach lead to bridge closed-loop identification with control performance assessment as well as data-driven controller parameters tuning. The paper proves that the optimization of the proposed criterion results in the unique optimal solution, which corresponds to the true plant and disturbance model parameters. In numerical examples, the proposed method is applied to datasets obtained from a continuous stirred tank reactor (CSTR), which is operated around an unstable steady state. The result illustrates the effectiveness of the proposed closed-loop identification method. Highlights: Closed-loop identification method derived from data-driven regulatory control. Simultaneously obtains the model parameters of the plant and the disturbance. Use routine operation data and require no extra experiment for identification. Bridge identification with performance assessment as well as data-driven control. Analyze a new variance criterion and prove existence of the unique optimal solution.