Control of nonlinear state-dependent systems using a receding horizon strategy

Abstract

A variety of nonlinear control design methods have been proposed for controlling severe nonlinear processes over the past three decades. The vast majority of approaches take a nonlinear affine representation of the system dynamics. It appears that many system dynamics can also be represented by a state-dependent model structure. Control of state-dependent systems has been investigated resulting in design methodologies such as state-dependent Riccati equation approach, state-dependent parameter and proportional–integral–plus approach, and the nonlinear generalized minimum variance approach. This article describes yet another approach based on a receding horizon strategy. Important results on optimal control are obtained. Implementation issues are also discussed. The proposed approach is validated through its application to a 25-tray binary distillation column process.