Continuous-Time identification of Linear Parameter Varying model using an output-error technique

Abstract

In this paper, a practical solution for the identification of Continuous-Time (CT) Input-Output (IO) Linear Parameter Varying (LPV) systems is proposed. For this particular class, we formulate an output error identification problem and present a parameter estimation scheme in which a prediction error based cost function is minimized using nonlinear programming. Because the cost function possesses local minima, the success of any iterative parameter estimation algorithm depends on appropriate initial seeds. One approach would be to generate an initial estimate using CT Reinitialized Partial Moments (RPM) models extended to CT IO LPV systems.We assume that the inputs, outputs and scheduling parameters are directly measurable, and that the functional dependence of the system coefficients on the parameters is of a polynomial form. The performance of the proposed method is evaluated by Monte Carlo simulation analysis. © 2011 IFAC.