Complex networked systems are the norm in the modern world with the human brain being one of the most complex networks. The control of such systems is a difficult task due to the interactions among the individual elements of the system. In this paper the design of sparse feedback controllers for complex networks is considered. Specifically, an H8 controller synthesis problem with D stability constraints is formulated and solved for networks with different topological features. This formulation allows us to examine tradeoffs between control performance, controller sparsity and speed of closed-loop response. We applied this formulation to synthetic networks and the Macaque visual cortical network, assuming Laplacian node dynamics. The results show that as the requested response becomes faster, the control performance improves, and the feedback gain matrix becomes sparser but with larger non-zero entries. This is analogous to the observation that functional brain networks during high cognitive demand adopt a more efficient but also costlier configuration. This analogy suggests a possible connection between cognitive control and closed-loop control under sparse feedback.
|Original language||English (US)|
|Title of host publication||2021 American Control Conference, ACC 2021|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - May 25 2021|
|Event||2021 American Control Conference, ACC 2021 - Virtual, New Orleans, United States|
Duration: May 25 2021 → May 28 2021
|Name||Proceedings of the American Control Conference|
|Conference||2021 American Control Conference, ACC 2021|
|City||Virtual, New Orleans|
|Period||5/25/21 → 5/28/21|
Bibliographical noteFunding Information:
* This work is supported by NSF OAC (award numbers 1938914 and 1940096). 1 Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55414, USA. 2 Department of Medicine, Harvard Medical School, 3Boston Children‘s Hospital, Boston MA, 02115, USA. P. Daoutidis is the corresponding author. firstname.lastname@example.org
© 2021 American Automatic Control Council.