Multiple Model Predictive Control Research Articles

Multiple-model predictive control of uncertain linear systems

In this paper, a multiple-model predictive control strategy is studied, which is based on a new model switching method. It combines multiple-model switching principle and predictive control method, and can be applied to the linear system with large model uncertainty. Simulation results show that the algorithm has good tracking and dynamic performance.

Successive one-step-ahead predictions in multiple model predictive control

This paper proposes a multiple model predictive control scheme for the case where the scheduling variable is the output of the nonlinear plant. By means of model interpolation and successive predictions, the resulting nonlinear strategy makes use of all models available for calculating future error predictions. Consistently good performance indicated by a simulated example confirms the usefulness of the proposed method.

Batch reactor control using a multiple model‐based controller design

AbstractThis work presents the development of a model‐based controller design called Multiple Model Predictive Control (MMPC) based on a set of linear, time‐varying, state space models to regulate batch processes according to multiple, pre‐specified reference profiles. Sufficient conditions for stability and boundedness of the dynamic evolution of the forced nonlinear system are provided. The performance of the MMPC design is demonstrated on a model of a batch reactor that represents the production of a polymer product.