We aim to increase the ability of coupled phase oscillators to maintain synchronization when the system is affected by stochastic disturbances. We model the disturbances by Gaussian noise and use the mean first hitting time when the state hits the boundary of a secure domain, that is a subset of the basin of attraction, to measure synchronization stability. Based on the invariant probability distribution of a system of phase oscillators subject to Gaussian disturbances, we propose an optimization method to increase the mean first hitting time and, thus, increase synchronization stability. In this method, a new metric for synchronization stability is defined as the probability of the state being absent from the secure domain, which reflects the impact of all the system parameters and the strength of disturbances. Furthermore, by this new metric, one may identify those edges that may lead to desynchronization with a high risk. A case study shows that the mean first hitting time is dramatically increased after solving corresponding optimization problems, and vulnerable edges are effectively identified. It is also found that optimizing synchronization by maximizing the order parameter or the phase cohesiveness may dramatically increase the value of the metric and decrease the mean first hitting time, thus decrease synchronization stability.

1.
D. M.
Abrams
and
S. H.
Strogatz
, “
Chimera states for coupled oscillators
,”
Phys. Rev. Lett.
93
,
174102
(
2004
).
2.
L.
Glass
and
M. C.
Mackey
,
From Clocks to Chaos: The Rhythms of Life
(
Princeton University Press
,
Princeton, NJ
,
1988
).
3.
C.
Ma
and
J.
Zhang
, “
Necessary and sufficient conditions for consensusability of linear multi-agent systems
,”
IEEE Trans. Autom. Control
55
,
1263
1268
(
2010
).
4.
T.
Yang
,
X.
Yi
,
J.
Wu
,
Y.
Yuan
,
D.
Wu
,
Z.
Meng
,
Y.
Hong
,
H.
Wang
,
Z.
Lin
, and
K. H.
Johansson
, “
A survey of distributed optimization
,”
Annu. Rev. Control
47
,
278
305
(
2019
).
5.
K.
Xi
,
J. L. A.
Dubbeldam
, and
H. X.
Lin
, “
Synchronization of cyclic power grids: Equilibria and stability of the synchronous state
,”
Chaos
27
,
013109
(
2017
).
6.
A. E.
Motter
,
S. A.
Myers
,
M.
Anghel
, and
T.
Nishikawa
, “
Spontaneous synchrony in power-grid networks
,”
Nat. Phys.
9
,
191
197
(
2013
).
7.
F.
Dörfler
and
F.
Bullo
, “
Synchronization in complex networks of phase oscillators: A survey
,”
Automatica
50
,
1539
1564
(
2014
).
8.
M.
Fazlyab
,
F.
Dörfler
, and
V. M.
Preciado
, “
Optimal network design for synchronization of coupled oscillators
,”
Automatica
84
,
181
189
(
2017
).
9.
P. S.
Skardal
,
D.
Taylor
, and
J.
Sun
, “
Optimal synchronization of complex networks
,”
Phys. Rev. Lett.
113
,
144101
(
2014
).
10.
L. M.
Pecora
and
T. L.
Carroll
, “
Master stability functions for synchronized coupled systems
,”
Phys. Rev. Lett.
80
,
2109
2112
(
1998
).
11.
P. J.
Menck
,
J.
Heitzig
,
N.
Marwan
, and
J.
Kurths
, “
How basin stability complements the linear-stability paradigm
,”
Nat. Phys.
9
,
89
92
(
2013
).
12.
R.
Delabays
,
M.
Tyloo
, and
P.
Jacquod
, “
The size of the sync basin revisited
,”
Chaos
27
,
103109
(
2017
).
13.
B. K.
Poolla
,
S.
Bolognani
, and
F.
Dörfler
, “
Optimal placement of virtual inertia in power grids
,”
IEEE Trans. Autom. Control
62
,
6209
6220
(
2017
).
14.
E.
Tegling
,
B.
Bamieh
, and
D. F.
Gayme
, “
The price of synchrony: Evaluating the resistive losses in synchronizing power networks
,”
IEEE Trans. Control Netw. Syst.
2
,
254
266
(
2015
).
15.
M.
Tyloo
,
T.
Coletta
, and
P.
Jacquod
, “
Robustness of synchrony in complex networks and generalized Kirchhoff indices
,”
Phys. Rev. Lett.
120
,
084101
(
2018
).
16.
K.
Xi
,
Z.
Wang
,
A.
Cheng
,
H. X.
Lin
,
J. H.
van Schuppen
, and
C.
Zhang
, “Synchronization of complex network systems with stochastic disturbances,” arXiv:2201.07213 (2022).
17.
M. M.
Klosek-Dygas
,
B. J.
Matkowsky
, and
Z.
Schuss
, “
Stochastic stability of nonlinear oscillators
,”
SIAM J. Appl. Math.
48
,
1115
1127
(
1988
).
18.
M.-L. T.
Lee
and
G. A.
Whitmore
, “
Threshold regression for survival analysis: Modeling event times by a stochastic process reaching a boundary
,”
Stat. Sci.
21
,
501
513
(
2006
).
19.
A.
Jadbabaie
,
N.
Motee
, and
M.
Barahona
, “On the stability of the Kuramoto model of coupled nonlinear oscillators,” in
Proceedings of the 2004 American Control Conference
(IEEE, 2004), Vol. 5, pp. 4296–4301.
20.
F.
Dörfler
and
F.
Bullo
, “
On the critical coupling for Kuramoto oscillators
,”
SIAM J. Appl. Dyn. Syst.
10
,
1070
1099
(
2011
).
21.
S.
Albeverio
and
V. N.
Kolokoltsov
, “
The rate of escape for some gaussian processes and the scattering theory for their small perturbations
,”
Stoch. Process Appl.
67
,
139
159
(
1997
).
22.
L. M.
Ricciardi
and
S.
Sato
, “
First-passage-time density and moments of the Ornstein-Uhlenbeck process
,”
J. Appl. Probab.
25
,
43
57
(
1988
).
23.
Y.
Kuramoto
,
Chemical Oscillations, Waves and Turbulence
(
Springer
,
New York
,
1984
).
24.
J. C.
Doyle
,
K.
Glover
,
P. P.
Khargonekar
, and
B. A.
Francis
, “
State-space solutions to standard H2 and H infinty control problems
,”
IEEE Trans. Autom. Control
34
,
831
847
(
1989
).
25.
R.
Toscano
,
Structured Controllers for Uncertain Systems
(
Springer-verlag
,
London
,
2013
).
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