## Abstract

The identification of structured state-space model has been intensively studied for a long time but still has not been adequately addressed. The main challenge is that the involved estimation problem is a non-convex (or bilinear) optimization problem. This paper is devoted to developing an identification

method which aims to find the global optimal solution under mild computational burden. Key to the developed identification algorithm is to transform a bilinear estimation to a rank constrained optimization problem and further a difference of convex programming (DCP) problem. The initial condition

for the DCP problem is obtained by solving its convex part of the optimization problem which happens to be a nuclear norm regularized optimization problem. Since the nuclear norm regularized optimization is the closest convex form of the low-rank constrained estimation problem, the obtained initial

condition is always of high quality which provides the DCP problem a good starting point. The DCP problem is then solved by the sequential convex programming method. Finally, numerical examples are included to show the effectiveness of the developed identification algorithm.

method which aims to find the global optimal solution under mild computational burden. Key to the developed identification algorithm is to transform a bilinear estimation to a rank constrained optimization problem and further a difference of convex programming (DCP) problem. The initial condition

for the DCP problem is obtained by solving its convex part of the optimization problem which happens to be a nuclear norm regularized optimization problem. Since the nuclear norm regularized optimization is the closest convex form of the low-rank constrained estimation problem, the obtained initial

condition is always of high quality which provides the DCP problem a good starting point. The DCP problem is then solved by the sequential convex programming method. Finally, numerical examples are included to show the effectiveness of the developed identification algorithm.

Original language | English |
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Title of host publication | Proceedings of the 2015 IEEE 54th Annual Conference on Decision and Control |

Editors | ME Valcher, Y Ohta, M Sampei |

Place of Publication | Piscataway, NJ, USA |

Publisher | IEEE Society |

Pages | 2737-2742 |

ISBN (Print) | 978-1-4799-7884-7 |

DOIs | |

Publication status | Published - 2015 |

Event | 54th IEEE Conference on Decision and Control, CDC 2015 - Osaka, Japan Duration: 15 Dec 2015 → 18 Dec 2015 Conference number: 54 |

### Conference

Conference | 54th IEEE Conference on Decision and Control, CDC 2015 |
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Abbreviated title | CDC 2015 |

Country/Territory | Japan |

City | Osaka |

Period | 15/12/15 → 18/12/15 |