Abstract
Input-Output data modelling using fully tuned radial basis function networks(RBF) for a tilt rotor aircraft experimental platform is presented in this paper. The behavior of the four degree-of-freedom platform exemplifies a high order nonlinear system with significant cross-coupling between longitudinal, latitudinal directional motions, and tilt rotor nacelles rolling movement. This paper develops a practical algorithm coupled with model validity tests for identifying nonlinear autoregressive moving average model with exogenous inputs(NARMAX). It is proved that input-output data modelling using fully tuned algorithm is suitable for modelling novelty configuration air vehicles. A procedure for system modelling was proposed in the beginning of this paper and the subsequent sections provided detailed descriptions on how each stage in the procedure could be realized. The effectiveness of this modelling procedure is demonstrated through the tilt rotor aircraft platform. The estimated model can be utilized for nonlinear flight simulation and control studies.
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Yu, C., Zhu, J., Che, J., Sun, Z. (2005). Input-Output Data Modelling Using Fully Tuned RBF Networks for a Four Degree-of-Freedom Tilt Rotor Aircraft Platform. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds) MICAI 2005: Advances in Artificial Intelligence. MICAI 2005. Lecture Notes in Computer Science(), vol 3789. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11579427_116
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DOI: https://doi.org/10.1007/11579427_116
Publisher Name: Springer, Berlin, Heidelberg
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