Nonlinear gas turbine modeling using NARMAX structures

The estimation of a nonlinear autoregressive moving average with exogenous inputs (NARMAX) model of an aircraft gas turbine is presented. A method is proposed whereby periodic signals with certain harmonic content are used to qualify the nature of the nonlinearity of the engine in the frequency domain. The static behavior of the engine is investigated in the time domain to approximate the order of nonlinearity and this information is used a priori to restrict the search space of the potential NARMAX models. A forward-regression orthogonal estimation algorithm is then employed to select the model terms using the error reduction ratio. The performance of the estimated NARMAX model is illustrated against a range of small- and large-signal engine tests