## Abstract

A network of agents with linear dynamics is strong structurally controllable if agents can be maneuvered from any initial state to any final state independently of the coupling strengths between agents. If a network is not strong structurally controllable with given input nodes (leaders), then the dimension of strong structurally controllable subspace quantifies the extent to which a network can be controlled by the same inputs. Computing this dimension exactly is computationally challenging. In this paper, we study the problem of computing a sharp lower bound on the dimension of strong structurally controllable subspace in networks with Laplacian dynamics. The bound is based on a sequence of vectors containing distances between leaders and the remaining nodes in the underlying network graph. Such vectors are referred to as the distance-to-leader vectors. We provide a polynomial time algorithm to compute a desired sequence of distance-to-leader vectors with a fixed set of leaders, which directly provides a lower bound on the dimension of strong structurally controllable subspace. We also present a linearithmic approximation algorithm to compute such a sequence, which provides near optimal solutions in practice. Finally, we numerically evaluate and compare our bound with other bounds in the literature on various networks.

Original language | English (US) |
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Title of host publication | 2019 IEEE 58th Conference on Decision and Control, CDC 2019 |

Publisher | Institute of Electrical and Electronics Engineers Inc. |

Pages | 5468-5473 |

Number of pages | 6 |

ISBN (Electronic) | 9781728113982 |

DOIs | |

State | Published - Dec 2019 |

Event | 58th IEEE Conference on Decision and Control, CDC 2019 - Nice, France Duration: Dec 11 2019 → Dec 13 2019 |

### Publication series

Name | Proceedings of the IEEE Conference on Decision and Control |
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Volume | 2019-December |

ISSN (Print) | 0743-1546 |

ISSN (Electronic) | 2576-2370 |

### Conference

Conference | 58th IEEE Conference on Decision and Control, CDC 2019 |
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Country/Territory | France |

City | Nice |

Period | 12/11/19 → 12/13/19 |

### Bibliographical note

Publisher Copyright:© 2019 IEEE.