A recursive modified partial least square aided data-driven predictive control with application to continuous stirred tank heater. (May 2020)

• Record Type: Journal Article
• Authors: Gao, Tianyi; Luo, Hao; Yin, Shen; Kaynak, Okyay
• Is Part Of: Journal of process control. Volume 89(2020)
• Page Start: 108
• Page End: 118
• Publication Date: 2020-05
• Subjects: Partial least square (PLS) -- Predictive control -- Data-driven -- Tracking control; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2020.03.004
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: The algorithm of recursive modified partial least square (RMPLS) is proposed and employed to enhance the precision of prediction with low computation complexity. The problem of signal prediction is equivalently transformed into parameter prediction with the help of locally weighted projection regression (LWPR), which makes the nonlinear quadratic problem of predictive controller for nonlinear process solvable. The proposed predictive control strategy is updated by learning from the online measurable data. It is therefore suitable for the tracking control of chemical industry with complex and changeable work conditions. Abstract: In this paper, a data-driven predictive control strategy for nonlinear system is proposed and testified on a continuous stirred tank heater (CSTH) benchmark. A recursive modified partial least square (RMPLS) algorithm is employed to regress the local linear model. The algorithm of locally weighted projection regression (LWPR) is then leveraged to build the predictive model, based on which a novel data-driven predictive control strategy is put forward. The proposed predictive controller has the ability to deal with changing working conditions, benefiting from the incremental learning ability of RMPLS and LWPR. The performance of the proposed control strategy is demonstrated with the CSTH while the superiority is illustrated by comparison with an existing model-free adaptive control approach.