Identification of non-linear system structure and parameters using regime decomposition
Automatica (Journal of IFAC)
Fuzzy model-based predictive control by instantaneous linearization
Fuzzy Sets and Systems
Multi-model predictive control based on the Takagi-Sugeno fuzzy models: a case study
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
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The paper addresses the application of Local Model Networks (LMN) in the field of identification and control of nonlinear systems. LMN are networks which are composed of locally accurate models, where output is interpolated by smooth locally active validity functions. This divide-and-conquer strategy is a general way of coping with complex systems. The architecture of LMN benefits from being able to incorporate a priori knowledge and conventional system identification methodology. The LMN structure also gives transparent and simple representation of the nonlinear system. In an initial offline identification phase the structure and parameters of local model have to be identified. The obtained global model is then used for prediction of future outputs in Model Predictive Control (MPC) of the pH neutralization plant.