Fuzzy Segregation-Based Identification and Control of Nonlinear Dynamic Systems

In this work, we propose a new method to model and control complex nonlinear dynamic systems. The suggested scheme employs an output curve methodology to determine the initial set of dynamic clustering spaces. The choice of the optimal dynamic clustering space is made through an analysis of cross validation performance and other indicative indices. A fuzzy clustering methodology for dynamic-model building is proposed. The relative advantages of the proposed adaptive fuzzy clustering method over other segregation methods are highlighted. For online control, a smooth internal model-switching strategy based on fuzzy methods is proposed and shown to be superior to other methods hitherto proposed in the literature. Two control structures based on the proposed methodology are discussed. The efficacy of the proposed fuzzy modeling and control schemes are demonstrated through several representative illustrative examples and by application to a high-purity distillation process.