An essential step towards the solution of many scientific problems is to accomplish modelling and identification of the system under investigation. This paper endeavours to establish an empirical relationship between input and observed output data for the identification of a one-degree-of-freedom hovering motion model of a twin rotor multi-input—multi-output system (TRMS). The TRMS can be perceived as a static test rig for an air vehicle with formidable control challenges. Therefore, modelling of aerodynamic function of the system is needed and carried out in both time and frequency domains based on observed input and output data pairs. Improved algorithms of recursive least square, genetic algorithm, and particle swarm optimization are proposed to develop a parametric model to mimic the behaviour of the twin rotor system in hovering mode. A complete system identification procedure is carried out, from experimental design to model validation using a laboratory-scale helicopter. In this case, the identified model is characterized by a fourth-order linear auto-regressive moving average structure, which describes with very high precision the hovering motion of a TRMS. Experimental results are obtained using a laboratory set-up system, confirming the viability and effectiveness of the proposed methodology.
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