TY - GEN
T1 - An efficient atomic norm minimization approach to identification of low order models
AU - Yilmaz, B.
AU - Lagoa, C.
AU - Sznaier, M.
PY - 2013
Y1 - 2013
N2 - Many practical situations involve synthesizing controllers for systems where a priori models are not available and thus must be identified from experimental data. In these cases it is of interest to identify the simplest model compatible with the available information, since the order of the model is usually reflected in the order of the resulting controllers. The main result of this paper is a computationally efficient algorithm to identify low order models from mixed time/frequency domain data. We propose two algorithms: one deterministic, based on semi-algebraic optimization, and the second based on a randomized approach. As shown here, both algorithms are guaranteed to converge to the optimum. A salient feature of the proposed approach is its ability to accommodate mixed time/frequency domain data without the need to resort to finite truncations or enforcing interpolation type constraints.
AB - Many practical situations involve synthesizing controllers for systems where a priori models are not available and thus must be identified from experimental data. In these cases it is of interest to identify the simplest model compatible with the available information, since the order of the model is usually reflected in the order of the resulting controllers. The main result of this paper is a computationally efficient algorithm to identify low order models from mixed time/frequency domain data. We propose two algorithms: one deterministic, based on semi-algebraic optimization, and the second based on a randomized approach. As shown here, both algorithms are guaranteed to converge to the optimum. A salient feature of the proposed approach is its ability to accommodate mixed time/frequency domain data without the need to resort to finite truncations or enforcing interpolation type constraints.
UR - http://www.scopus.com/inward/record.url?scp=84902312801&partnerID=8YFLogxK
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U2 - 10.1109/CDC.2013.6760809
DO - 10.1109/CDC.2013.6760809
M3 - Conference contribution
AN - SCOPUS:84902312801
SN - 9781467357173
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 5834
EP - 5839
BT - 2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 52nd IEEE Conference on Decision and Control, CDC 2013
Y2 - 10 December 2013 through 13 December 2013
ER -