A system identification method for nonlinear systems with unknown structure by means of short input-output data is proposed. This method introduces more general model structure for nonlinear systems. Moreover, based on gray-box idea and its salient feature with expanding NARMAX (Nonlinear Autoregressive, Moving Average eXogenous) modeling, this method integrates different system information. Th...

This paper presents controllers based on linear and nonlinear models of an aircraft gas turbine engine. These models along with the performance indexes, ITAE, IAE and IES are used to estimate the parameters of a PID controller. The parameters and hence the quality of the controllers are very much dependent on the accuracy of the models. It is shown that the Nonlinear AutoRegressive Moving Avera...

We propose a new algorithm for estimating NARMAX models with L1 regularization for models represented as a linear combination of basis functions. Due to the L1-norm penalty the Lasso estimation tends to produce some coefficients that are exactly zero and hence gives interpretable models. The novelty of the contribution is the inclusion of error regressors in the Lasso estimation (which yields a...

In this paper, nonlinear dynamical black-box models of a common rail injection system for a CNG engine are developed. In particular, the common rail pressure dynamics is modeled on the basis of three input signals, easily and cheaply measurable on board a vehicle. The nonlinear model is identified by means of Multi Layer Perceptron neural networks. Both non-autoregressive (NMAX) and autoregress...

This paper presents PID controller designs based on NARMAX and feedforward neural network models of a Spey gas turbine engine. Both models represent the dynamic relationship between the fuel flow and shaft speed. Due to the engine non-linearity, a single set of PID controller parameters is not sufficient to control the gas turbine throughout the operating range. Gain-scheduling PID controllers ...

The discrete-time system of multilayer composite plate is modeled using neural network (NN) to produce a nonlinear exogenous autoregressive moving-average model (NARMAX). The model is implemented by training a NN with input-output experimental data. Each damaged sample can be modeled by a parameter governed by the propagation behaviors of the NN. A residual signal is evaluated from the differen...

This paper presents an improved on-line identification method of non-linear time-varying dynamic systems with linear and non-linear models . This method is based on Genetic algorithms with a new technique to simulate the behaviour of the gradient method without using the concept of derivatives. This method uses an on-line identification algorithm that begins by calculating what ARX model adapts...