A Novel Approach for Online Multivariable Evolving Fuzzy Modeling from Experimental Data
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Abstract
In this paper an online evolving fuzzy Takagi-Sugeno state-space model identification approach for multivariable dynamic systems, is proposed. The proposed methodology presents an evolving fuzzy clustering algorithm based on the concept of Recursive Density Estimation for online antecedent structure adaptation according to the data. For estimation of the minimum realization state-space models in the consequent of the fuzzy rules, is proposed a recursive methodology based on the Eigensystem Realization Fuzzy Algorithm using the system fuzzy Markov parameters obtained recursively from experimental data. Experimental results from the trajectory prediction of a training rocket, are presented.
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Jul 2018
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Published: 08 July 2018
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