Hybrid Fuzzy Modelling for Model Predictive Control

Authors:
Gorazd Karer;Gašper Mušič;Igor Škrjanc;Borut Zupančič
Affiliations:
Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia 1000;Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia 1000;Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia 1000;Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia 1000
Venue:
Journal of Intelligent and Robotic Systems
Year:
2007

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Abstract

Model predictive control (MPC) has become an important area of research and is also an approach that has been successfully used in many industrial applications. In order to implement a MPC algorithm, a model of the process we are dealing with is needed. Due to the complex hybrid and nonlinear nature of many industrial processes, obtaining a suitable model is often a difficult task. In this paper a hybrid fuzzy modelling approach with a compact formulation is introduced. The hybrid system hierarchy is explained and the Takagi---Sugeno fuzzy formulation for the hybrid fuzzy modelling purposes is presented. An efficient method for identifying the hybrid fuzzy model is also proposed. A MPC algorithm suitable for systems with discrete inputs is treated. The benefits of the MPC algorithm employing the hybrid fuzzy model are verified on a batch-reactor simulation example: a comparison between the proposed modern intelligent (fuzzy) approach and a classic (linear) approach was made. It was established that the MPC algorithm employing the proposed hybrid fuzzy model clearly outperforms the approach where a hybrid linear model is used, which justifies the usability of the hybrid fuzzy model. The hybrid fuzzy formulation introduces a powerful model that can faithfully represent hybrid and nonlinear dynamics of systems met in industrial practice, therefore, this approach demonstrates a significant advantage for MPC resulting in a better control performance.