National Instruments 370760B-01 Calculator User Manual


 
BIBLIOGRAPHY 195
[24] D. L. Laughlin, K. G. Jordan, and M. Morari, “Internal model control and process
uncertainty: mapping uncertainty regions for SISO controller design,” Int. J. of
Control, vol. 44, no. 6, pp. 1675–1698, 1986.
[25] R. S. Smith and M. Dahleh, eds., The Modeling of Uncertainty in Control Systems:
Proceedings of the 1992 Santa Barbara Workshop. 391 pgs., Springer-Verlag, 1994.
[26] M. Gevers, “Connecting identification and robust control: A new challenge,” in
Proc. IFAC Symp. on Identification & System Parameter Estimation,vol.1,
pp. 1–10, 1991.
[27] A. Helmicki, C. Jacobson, and C. Nett, “H
identification of stable lsi systems: A
scheme with direct application to controller design,” Proc. Amer. Control Conf.,
pp. 1428–1434, 1989.
[28] G. Gu and P. P. Khargonekar, “Linear and nonlinear algorithms for identification in
H
with error bounds,” in Proc. Amer. Control Conf., pp. 64–69, 1991.
[29] A. J. Helmicki, C. A. Jacobson, and C. N. Nett, “Control oriented system
identification: A worst-case/deterministic approach in H
,” IEEE Trans. Auto.
Control, pp. 1163–1176, 1991.
[30] P. M¨akil¨a and J. Partington, “Robust approximation and identification in H
,”
Proc. Amer. Control Conf., pp. 70–76, 1991.
[31] G. Gu and P. P. Khargonekar, “Linear and nonlinear algorithms for identification in
H
with error bounds,” in IEEE Trans. Auto. Control, vol. 37, pp. 953–963, 1992.
[32] G. Gu and P. P. Khargonekar, “A class of algorithms for identification in H
,” in
Automatica, vol. 28, pp. 299–312, 1992.
[33] G. Gu, P. P. Khargonekar, and Y. Li, “Robust convergence of two-stage nonlinear
algorithms for identification in H
,” in Syst. and Control Letters, vol. 18,
pp. 253–263, 1992.
[34] R. G. Hakvoort, “Worst-case system identification in H
: error bounds and
optimal models,” in Selected Topics in Identification Modelling and Control,Delft
University Press, Vol. 5 1992.
[35] E.-W. Bai, “On-line H
2
, H
and pointwise uncertainty bound quantification in
identification of restricted complexity models,” in Proc. IEEE Control Decision
Conf., pp. 1719–1724, 1992.
[36] G. Goodwin and M. Salgado, “Quantification of uncertainty in estimation using an
embedding principle,” in Proc. Amer. Control Conf., 1989.