Abstract
Many practical applications, such as the fuel control of a gas turbine engine, can be modeled by a feedback connection of a linear controller in series with a Hammerstein system, where the nonlinearity provides a representation of the control element or actuator. An iterative gradient-based method is proposed to simultaneously identify the nonlinear fuel valve characteristic and a low-order linear plant model in gas turbine applications that leverages a priori knowledge of both the nonlinearity and engine dynamics. The identification is a nonlinear prediction error minimization method in a closed-loop Hammerstein model framework. It is applied to data from a high-fidelity simulation of a 5 megawatt TaurusTM 60 industrial gas turbine.
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