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A novel scheme for the identification of nonlinear flow control process based on fuzzy tuning parameters

This paper is devoted to a rigorous conjecture concerning the identification of a process with nonlinear dynamics, where the nonlinearities are stimulated by an intended variation in set point. The changing of the operating point motivates the process nonlinearity and cause considerable challenges for process modeling. This paper proposes a novel scheme to overcome these challenges. The technique utilizes the Parameter Estimation Method (PEM) with Gauss Newton (GN) algorithm to obtain the optimal values of the model parameters at selective operating points. Based on the set value, a fuzzy synthesizer determines the input-dependent parameters to construct a nonlinear model. Experimental study on the Process Control System (PCS) training set is employed to demonstrate the effectiveness of the proposed technique. The steady state I/O mapping is used to determine the level of congruence between the process output and the output of the proposed nonlinear model.