1,352 research outputs found
Topological and Graph-coloring Conditions on the Parameter-independent Stability of Second-order Networked Systems
In this paper, we study parameter-independent stability in qualitatively
heterogeneous passive networked systems containing damped and undamped nodes.
Given the graph topology and a set of damped nodes, we ask if output consensus
is achieved for all system parameter values. For given parameter values, an
eigenspace analysis is used to determine output consensus. The extension to
parameter-independent stability is characterized by a coloring problem, named
the richly balanced coloring (RBC) problem. The RBC problem asks if all nodes
of the graph can be colored red, blue and black in such a way that (i) every
damped node is black, (ii) every black node has blue neighbors if and only if
it has red neighbors, and (iii) not all nodes in the graph are black. Such a
colored graph is referred to as a richly balanced colored graph.
Parameter-independent stability is guaranteed if there does not exist a richly
balanced coloring. The RBC problem is shown to cover another well-known graph
coloring scheme known as zero forcing sets. That is, if the damped nodes form a
zero forcing set in the graph, then a richly balanced coloring does not exist
and thus, parameter-independent stability is guaranteed. However, the full
equivalence of zero forcing sets and parameter-independent stability holds only
true for tree graphs. For more general graphs with few fundamental cycles an
algorithm, named chord node coloring, is proposed that significantly
outperforms a brute-force search for solving the NP-complete RBC problem.Comment: 30 pages, accepted for publication in SICO
Uncertainty in Soft Temporal Constraint Problems:A General Framework and Controllability Algorithms forThe Fuzzy Case
In real-life temporal scenarios, uncertainty and preferences are often
essential and coexisting aspects. We present a formalism where quantitative
temporal constraints with both preferences and uncertainty can be defined. We
show how three classical notions of controllability (that is, strong, weak, and
dynamic), which have been developed for uncertain temporal problems, can be
generalized to handle preferences as well. After defining this general
framework, we focus on problems where preferences follow the fuzzy approach,
and with properties that assure tractability. For such problems, we propose
algorithms to check the presence of the controllability properties. In
particular, we show that in such a setting dealing simultaneously with
preferences and uncertainty does not increase the complexity of controllability
testing. We also develop a dynamic execution algorithm, of polynomial
complexity, that produces temporal plans under uncertainty that are optimal
with respect to fuzzy preferences
A geometrical approach to control and controllability of nonlinear dynamical networks
Acknowledgements This work was supported by the ARO under Grant No. W911NF-14-1-0504. X.W. was supported by the NIH under Grant No. GM106081.Peer reviewedPublisher PD
A geometrical approach to control and controllability of nonlinear dynamical networks
abstract: In spite of the recent interest and advances in linear controllability of complex networks, controlling nonlinear network dynamics remains an outstanding problem. Here we develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another, assuming that the former is undesired and the latter is desired. To make our framework practically meaningful, we consider restricted parameter perturbation by imposing two constraints: it must be experimentally realizable and applied only temporarily. We introduce the concept of attractor network, which allows us to formulate a quantifiable controllability framework for nonlinear dynamical networks: a network is more controllable if the attractor network is more strongly connected. We test our control framework using examples from various models of experimental gene regulatory networks and demonstrate the beneficial role of noise in facilitating control.The final version of this article, as published in Nature Communications, can be viewed online at: https://www.nature.com/articles/ncomms1132
- …