Fuzzy Aggregation Based Multiple Models Explicit Multi Parametric MPC Design for a Quadruple Tank Process

In this study, design of fuzzy aggregated multi parametric model predictive controller (mpMPC) for multivariable systems is proposed. Using first principles model (FPM) of the process, a steady state map (SSM) of input output is generated. A minimum number of linearized models are obtained from the FPM in different nominal operating regions to satisfy prediction error criterion. From gap metric measure, these models are clustered by k-means algorithm, followed by cluster representatives (CR) selection and fuzzy weights (to represent each model using aggregated CR's) calculation. For a given operating point, fuzzy weights are determined by a supervisory structure. Using these weights, the estimation model and control output (from mpMPC's designed for each CR) are aggregated. A real time quadruple tank (QT) is controlled using the proposed strategy, which is implemented on an embedded platform. Performance metrics indicate a 20% improvement in prediction and 20% improvement in control under closed loop using proposed strategy.