A hybrid Gaussian process approach to robust economic model predictive control. (August 2020)

• Record Type: Journal Article
• Authors: Rostam, Mohammadreza; Nagamune, Ryozo; Grebenyuk, Vladimir
• Is Part Of: Journal of process control. Volume 92(2020)
• Page Start: 149
• Page End: 160
• Publication Date: 2020-08
• Subjects: Economic model predictive control -- Hybrid Gaussian process -- Long-term forecasting -- Unknown disturbances; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2020.06.006
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: This paper proposes a hybrid Gaussian process (GP) approach to robust economic model predictive control under unknown future disturbances in order to reduce the conservatism of the controller. The proposed hybrid GP is a combination of two well-known methods, namely, kernel composition and nonlinear auto-regressive. A switching mechanism is employed to select one of these methods for disturbance prediction after analyzing the prediction outcomes. The hybrid GP is intended to detect not only patterns but also unexpected behaviors in the unknown disturbances by using past disturbance measurements. A novel forgetting factor concept is also utilized in the hybrid GP, giving less weight to older measurements, in order to increase prediction accuracy based on recent disturbances values. The detected disturbance information is used to reduce prediction uncertainty in economic model predictive controllers systematically. The simulation results show that the proposed method can improve the overall performance of an economic model predictive controller compared to other GP-based methods in cases when disturbances have discernible patterns. Highlights: Hybrid Gaussian process approach to improve robust economic model predictive control. Disturbance estimator by combining two well-known methods. Proposing a condition to identify inaccurate disturbance predictions. Proposing a forgetting factor term for Gaussian process models. Reducing the cost function while decreasing constrains violations.