论文信息 - Error bounds for conditional algorithms in restricted complexity set membership identification

Error bounds for conditional algorithms in restricted complexity set membership identification

Restricted complexity estimation is a major topic in control-oriented identification. Conditional algorithms are used to identify linear finite-dimensional models of complex systems, the aim being to minimize the worst-case identification error. High computational complexity of optimal solutions suggests employing suboptimal estimation algorithms. The paper studies different classes of conditional estimators and provides results that assess the reliability level of suboptimal algorithms.

Andrea Garulli | Antonio Vicino | Giovanni Zappa | Boleslaw Z. Kacewicz

[1] Andrea Garulli,et al. Conditional central algorithms for worst-case estimation and filtering , 1997, Proceedings of the 36th IEEE Conference on Decision and Control.

[2] Graham C. Goodwin,et al. Estimated Transfer Functions with Application to Model Order Selection , 1992 .

[3] Paul M.J. Van den Hof,et al. Identification and Control - Closed Loop Issues , 1994 .

[4] Carl N. Nett,et al. Control oriented system identification: a worst-case/deterministic approach in H/sub infinity / , 1991 .

[5] Laura Giarré,et al. Model quality evaluation in set membership identification , 1997, Autom..

[6] Antonio Vicino,et al. Conditionally optimal algorithms and estimation of reduced order models , 1988, J. Complex..

[7] Boleslaw Z. Kacewicz. Worst-case conditional system identification in a general class of norms , 1999, Autom..

[8] P.M. Mäkilä,et al. Worst-case control-relevant identification , 1995, Autom..

[9] Paul M. J. Van den Hof,et al. Identification and control - Closed-loop issues , 1995, Autom..