Model predictive control of a granulation system using soft output constraints and prioritized control objectives

A granulation system presented by Pottman et al. [J. Powder Technol., 108 ( 2) (2000) 192] is used to demonstrate two Model Predictive Control (MPC) co ntrol methods. The first method penalizes process output constraint violati ons using soft constraints in the objective function. It is found that the soft constraints must be much tighter than the actual constraints for effec tive control of the granulation system. The soft constraint formulation is presented as a variation of the asymmetric objective function formulation d escribed by Parker et al. [Proc. American Control Conf. Chicago, IL (2000)] . The second control method is based on the prioritized objective formulati on originally proposed by Tyler and Morari [Automatica 35 (1999) 565]. The prioritized objective method uses optimization constraints involving binary variables to explicitly represent and prioritize control objectives. The f ormulation presented in this article demonstrates a multi-level objective f unction which first maximizes the number of objectives satisfied in order o f priority, then maximizes the number of total objectives, and finally mini mizes the traditional MPC error tracking and move suppression terms. This p rioritized objective formulation also allows for delayed implementation of output objective constraints, allowing for relaxation of control objectives . (C) 2001 Elsevier Science B.V. All rights reserved.