Globally optimal nonlinear model predictive control based on multi-parametric disaggregation. (April 2017)

• Record Type: Journal Article
• Authors: Wang, Xiaoqiang; Mahalec, Vladimir; Qian, Feng
• Is Part Of: Journal of process control. Volume 52(2017)
• Page Start: 1
• Page End: 13
• Publication Date: 2017-04
• Subjects: Globally optimal nonlinear model predictive control -- Multi-parametric disaggregation -- Global optimization; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2017.01.005
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: Globally optimal nonlinear model predictive control. Globally optimal control based on multiparametric disaggregation. Computational robustness via optimality based bound tightening. Abstract: Nonlinear model predictive control is appropriate for controlling highly nonlinear processes, particularly when operating conditions change frequently. If the problem is nonconvex, the controller must lead the process to a global, rather than a local optimum. This work deals with computation of the control actions which lead to the global optimum via the normalized multi-parametric disaggregation technique. The continuous process model is transformed into a nonlinear programming (NLP) problem via discretization which uses an implicit integration method. The NLP problem is relaxed into a mixed integer linear programming (MILP) model. Iterations between solving MILP (lower bound) and using its solution as a starting point for a local nonlinear optimizer (which computes the upper bound) continue until the gap is closed (an l 1 -norm objective function is used). Controller performance is illustrated by several examples. Relative simplicity of the algorithm makes it possible to be implemented by a wide audience.