Dynamically scheduled MPC of nonlinear processes using hinging hyperplane models

Abstract

This article deals with the issues associated with designing scheduled model predictive controllers for nonlinear systems within the multiple-linear-model-based control framework. The issues of model set generation from empirical data and closed-loop application of the generated model set are considered. A method of hinging hyperplanes is proposed as a way to construct a piecewise linens dynamic model, conducive to dynamic scheduling of linear MPC controllers. The design and implementation of dynamically scheduled MPC using a hinge function model are discussed, as well as its advantages. Alternate MPC formulations considered here require more computation, but utilize the hinge function model as a global model. Simulated examples of isothermal CSTRs and a batch fermenter are also presented to illustrate the proposed methodologies.