Model switching method of multi-hierarchical model predictive control system

Multi-model predictive control has become an effective method for handling the process of nonlinear system. But the system using traditional multi-model predictive control has slow rise time and slow convergence speed when it is used for the MIMO nonlinear system solving the condition with large scale transition of operating condition. To solve these problems, a new structure of multi-model called multi-hierarchical model has been presented. This structure consists of many layers that each layer is comprised of multiple models. The number of sub-models in each layer is different. Under the condition of the same global operation space, the upper layer has a smaller number of sub-models, and the lower layer has a larger number of sub-models. Because of this structure, the models chosen from different layers can deal with the large scale transition of operating condition. In this paper, a new model switching method between different layers is presented. This method uses the error of output and the variation of output error as the rules for layer switching. In the end of this paper, the simulation results of pH neutralization process which is a MIMO nonlinear system demonstrate that the multi-hierarchical model using the new model switching method is superior to single-hierarchical model with faster rise time, better convergence speed and stability. © All Rights Reserved.