Multi-Hierarchical Model Predictive Control Based on K-Means Clustering Algorithms
Multi-model predictive control has become an effective method for nonlinear system. But the traditional multi-model has large tracking error compared with desired output when it is used to solve the operating condition with large scale transition. To solve this problem, this paper presents a new structure of multi-model called multi-hierarchical model. The new structure consists of many layers that each layer is constituted of different number of multiple models. In each layer, multi-model is obtained by partial least squares method after k-means clustering algorithm divides the global working spaces into desired parts. Because of this special structure, the models chose from different layers can deal with the operating condition changed with large scale. At the end of this paper, experiments are carried on the pH neutralization process which is a MIMO nonlinear system and the simulation results demonstrate that the multi-hierarchical model is superior to single-hierarchical model with smaller model tracking error faster convergence speed and better stability.
L. L. Liu and L. F. Zhou, "Multi-Hierarchical Model Predictive Control Based on K-Means Clustering Algorithms", Advanced Materials Research, Vols. 211-212, pp. 147-151, 2011