LPV system identification using a separable least squares support vector machines approach

@article{Santos2014LPVSI,
  title={LPV system identification using a separable least squares support vector machines approach},
  author={Paulo J. Lopes dos Santos and T.-P. Azevedo-Perdico{\'u}lis and Jos{\'e} A. Ramos and Sunil Deshpande and Daniel E. Rivera and Jorge Leite Martins de Carvalho},
  journal={53rd IEEE Conference on Decision and Control},
  year={2014},
  pages={2548-2554}
}
In this article, an algorithm to identify LPV State Space models for both continuous-time and discrete-time systems is proposed. The LPV state space system is in the Companion Reachable Canonical Form. The output vector coefficients are linear combinations of a set of a possibly infinite number of nonlinear basis functions dependent on the scheduling signal, the state matrix is either time invariant or a linear combination of a finite number of basis functions of the scheduling signal and the… CONTINUE READING

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Identification of LPV systems with LFT parametric dependence via convex optimization

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