New schemes and theoretical analysis of the master-slave family of recursive identification algorithms

An analysis of the Master-Slave family of system identification algorithms is developed for the white noise input case. These methods try to avoid the biased parameter estimation problem of equation error methods in the presence of noisy data, while keeping the quadratic nature of the error variances to be minimized (a feature that is lost with the output error formulation). The Master-Slave techniques may be seen as off-line schemes that construct a sequence of equation error optimization problems, each one based on the solution to the previous one. Some conditions for the stationary points of the iteration are given. Examples are presented for plants with poles close to z=1 where undesirable attractive convergent points arise. An alternative scheme based on interpolation conditions rather than minimizing error variances is proposed and shown to have a single stationary point corresponding to the plant parameters. Finally, a simplified structure and a lattice algorithm are presented for on-line implementation. (C) 1997 Elsevier Science B.V.