TY - JOUR
T1 - On H∞ model reduction using LMIs
AU - Ebihara, Yoshio
AU - Hagiwara, Tomomichi
N1 - Funding Information:
Manuscript received October 1, 2003; revised April 2, 2004. Recommended by Associate Editor E. Jonckheere. This work supported in part by the Ministry of Education, Culture, Sports, Science and Technology of Japan under Grant-in-Aid for Young Scientists (B), 15760314.
PY - 2004/7
Y1 - 2004/7
N2 - In this note, we deal with the problem of approximating a given nth-order linear time-invariant system G by an rth-order system Gr where r < n. It is shown that lower bounds of the H∞ norm of the associated error system can be analyzed by using linear matrix ineqaulity (LMI)-related techniques. These lower bounds are given in terms of the Hankel singular values of the system G and coincide with those obtained in the previous studies where the analysis of the Hankel operators plays a central role. Thus, this note provides an alternative proof for those lower bounds via simple algebraic manipulations related to LMIs. Moreover, when we reduce the system order by the multiplicity of the smallest Hankel singular value, we show that the problem is essentially convex and the optimal reduced-order models can be constructed via LMI optimization.
AB - In this note, we deal with the problem of approximating a given nth-order linear time-invariant system G by an rth-order system Gr where r < n. It is shown that lower bounds of the H∞ norm of the associated error system can be analyzed by using linear matrix ineqaulity (LMI)-related techniques. These lower bounds are given in terms of the Hankel singular values of the system G and coincide with those obtained in the previous studies where the analysis of the Hankel operators plays a central role. Thus, this note provides an alternative proof for those lower bounds via simple algebraic manipulations related to LMIs. Moreover, when we reduce the system order by the multiplicity of the smallest Hankel singular value, we show that the problem is essentially convex and the optimal reduced-order models can be constructed via LMI optimization.
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U2 - 10.1109/TAC.2004.831116
DO - 10.1109/TAC.2004.831116
M3 - Article
AN - SCOPUS:3843117737
SN - 0018-9286
VL - 49
SP - 1187
EP - 1191
JO - IRE Transactions on Automatic Control
JF - IRE Transactions on Automatic Control
IS - 7
ER -