A multiple model predictive control strategy in the PLS framework. (January 2015) - Chi, Qinghua; Liang, Jun
Highlights: Propose a switched multiple model predictive control strategy in the PLS framework. Propose a switching criterion based on T 2 statistic of the measured outputs. The stability of the proposed MMPC strategy is discussed. The proposed MMPC strategy performs well over large operating ranges. Abstract: For chemical processes with a wide range of operating conditions, a switched multiple model predictive control (MMPC) strategy in the partial least squares (PLS) framework is proposed. Interactive MIMO systems can be automatically decoupled with inputs and outputs paired in their dynamic PLS models. Based on the identified PLS models, companion controllers are designed to form the MMPC strategy. A novel switching criterion based on output statistics is proposed to assure each model/control pair works in its operating region spanned by the identification data sets. The control results of disturbance rejection and setpoint tracking in a two-phase chemical reactor process are presented to demonstrate the capability and effectiveness of the proposed MMPC strategy.