Globally optimal nonlinear model predictive control based on multi-parametric disaggregation. (April 2017) - Wang, Xiaoqiang; Mahalec, Vladimir; Qian, Feng
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.