Adaptive horizon economic nonlinear model predictive control. (August 2020)

• Record Type: Journal Article
• Authors: Krishnamoorthy, Dinesh; Biegler, Lorenz T.; Jäschke, Johannes
• Is Part Of: Journal of process control. Volume 92(2020)
• Page Start: 108
• Page End: 118
• Publication Date: 2020-08
• Subjects: Economic model predictive control -- Fast MPC -- Sensitivity -- Dynamic optimization -- Process control; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2020.05.013
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: In this paper, we present a computationally efficient economic NMPC formulation, where we propose to adaptively update the length of the prediction horizon in order to reduce the problem size. This is based on approximating an infinite horizon economic NMPC problem with a finite horizon optimal control problem with terminal region of attraction to the optimal equilibrium point. Using the nonlinear programming (NLP) sensitivity calculations, the minimum length of the prediction horizon required to reach this terminal region is determined. We show that the proposed adaptive horizon economic NMPC (AH-ENMPC) has comparable performance to standard economic NMPC (ENMPC). We also show that the proposed adaptive horizon economic NMPC framework is nominally stable. Two benchmark examples demonstrate that the proposed adaptive horizon economic NMPC provides similar performance as the standard economic NMPC with significantly less computation time. Highlights: Computationally efficient economic NMPC formulation with adaptive horizon length. Nominal stability of the adaptive horizon economic NMPC is shown. Two examples demonstrate that the proposed adaptive horizon economic NMPC provides similar performance as the standard economic NMPC with significantly less computation time.