Intelligent multi-resolution modelling: Application to synthetic jet actuation and flow control

Puneet Singla, John L. Junkins, Othon Rediniotis, Kamesh Subbarao

Research output: Contribution to conferencePaper

3 Scopus citations

Abstract

A novel "directed graph" based algorithm is presented that facilitates intelligent learning and adaptation of the parameters appearing in a Radial Basis Function Network (RBFN) description of input output behavior of nonlinear dynamical systems. Several alternate formulations, that enforce minimal parameterization of the RBFN parameters are presented. An Extended Kalman Filter algorithm is incorporated to estimate the model parameters using multiple windows of the batch input-output data. The efficacy of the learning algorithms are evaluated on judiciously constructed test data before implementing them on real aerodynamic lift and pitching moment data obtained from experiments on a Synthetic Jet Actuation based Smart Wing.

Original languageEnglish (US)
Pages8870-8883
Number of pages14
StatePublished - Jul 1 2004
Event42nd AIAA Aerospace Sciences Meeting and Exhibit - Reno, NV, United States
Duration: Jan 5 2004Jan 8 2004

Other

Other42nd AIAA Aerospace Sciences Meeting and Exhibit
CountryUnited States
CityReno, NV
Period1/5/041/8/04

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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    Singla, P., Junkins, J. L., Rediniotis, O., & Subbarao, K. (2004). Intelligent multi-resolution modelling: Application to synthetic jet actuation and flow control. 8870-8883. Paper presented at 42nd AIAA Aerospace Sciences Meeting and Exhibit, Reno, NV, United States.