El
Aissi H.
Messaoud ABSTRACT This paper focuses on the Model Based Predictive Control (MBPC) of the humidifying process inside a drying blower. This process is known to have a nonlinear behavior. To synthesize the MBP control, we used a Reproducing Kernel Hilbert Space (RKHS) model with reduced complexity. The identification of this model is carried in a black box context with no a priori information needed, using the Statistical Learning Techniques (SLT). This model is linear with respect to its parameters and copes well with the nonlinear systems approximation problems. The proposed algorithm is used to regulate the humidity inside a drying blower.
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