System identification for helicopter longitudinal dynamics model - best practices

For estimation of mathematical model and parameters of a system, the system identification has been widely applied in various domains such as the automatic control, aviation, spaceflight, civil and mechanical engineering, medicine, biology, chemical processes, marine ecology, geology etc. The main aim of this work is to perform preliminary studies to design a control law for helicopter model making it as autopilot. X-plane flight simulator will be used with Matlab wherein the estimated model is imported and simulated for its practical behavior. A longitudinal state-space model of the Puma, SA330 research helicopter is used as a reference model. First, the model is described and with standard reference input test signals, output data set is generated, then this input-output dataset is used for system identification purpose. Both traditional methods such as subspace & prediction-error minimization (PEM) method along with modern ways of identification methods such as neural networks are used. A practical comparison between used identification methods and best suitable type of input for estimation is discussed.