Interpolating Optimizing Process Control

In this paper a new model-based optimizing controller for a set of nonlinear systems is proposed. The nonlinear model set is based on a convex combination of two bounding linear models. An optimal control sequence is computed for each of the two bounding models. The proposed control algorithm is based on a convex combination of the two control sequences. A novel feature in these two optimizations is an added constraint related to the feasibility of the 'other' bounding model. The control algorithm can for example be used in model predictive control. We provide robust feasibility guarantees and an upper bound on the optimal criterion if the bounding models are linear FIR models. Further, simulation examples demonstrate signiicant feasibility improvements in the case where the bounding models are general linear state-space models. The proposed method guarantees robust feasibility for a 1-step ahead prediction in the general case. This can be of interest in MPC applications.