Recursive identification algorithms for continuous systems using an adaptive procedure

Abstract

Recursive identification algorithms for continuous systems from sampled input and output data are discussed. The continuous system is identified through an approximated discrete-time estimation model with continuous system parameters. An approximated discrete-time model of the continuous system under study is first obtained by bilinear transformation. Then using the estimated denominator of the transfer function of the discrete-time model to construct adaptive IIR filters introduced to avoid direct approximations of differentiations from sampled data, an approximated discrete-time estimation model with continuous system parameters is derived. The discrete-time estimation model is composed of filtered sampled system input and output signals. With filtered inputs and delayed filtered outputs as instrumental variables, some kinds of recursive instrumental variable identification algorithms are proposed to obtain consistent estimates in the presence of noise. The proposed identification algorithms have close relations to standard recursive identification algorithms for common discrete-time systems. Numerical examples are included to illustrate the effectiveness of the recursive identification algorithms.