3,637 research outputs found
Distributed convex optimization via continuous-time coordination algorithms with discrete-time communication
This paper proposes a novel class of distributed continuous-time coordination
algorithms to solve network optimization problems whose cost function is a sum
of local cost functions associated to the individual agents. We establish the
exponential convergence of the proposed algorithm under (i) strongly connected
and weight-balanced digraph topologies when the local costs are strongly convex
with globally Lipschitz gradients, and (ii) connected graph topologies when the
local costs are strongly convex with locally Lipschitz gradients. When the
local cost functions are convex and the global cost function is strictly
convex, we establish asymptotic convergence under connected graph topologies.
We also characterize the algorithm's correctness under time-varying interaction
topologies and study its privacy preservation properties. Motivated by
practical considerations, we analyze the algorithm implementation with
discrete-time communication. We provide an upper bound on the stepsize that
guarantees exponential convergence over connected graphs for implementations
with periodic communication. Building on this result, we design a
provably-correct centralized event-triggered communication scheme that is free
of Zeno behavior. Finally, we develop a distributed, asynchronous
event-triggered communication scheme that is also free of Zeno with asymptotic
convergence guarantees. Several simulations illustrate our results.Comment: 12 page
Distributed Event-Based State Estimation for Networked Systems: An LMI-Approach
In this work, a dynamic system is controlled by multiple sensor-actuator
agents, each of them commanding and observing parts of the system's input and
output. The different agents sporadically exchange data with each other via a
common bus network according to local event-triggering protocols. From these
data, each agent estimates the complete dynamic state of the system and uses
its estimate for feedback control. We propose a synthesis procedure for
designing the agents' state estimators and the event triggering thresholds. The
resulting distributed and event-based control system is guaranteed to be stable
and to satisfy a predefined estimation performance criterion. The approach is
applied to the control of a vehicle platoon, where the method's trade-off
between performance and communication, and the scalability in the number of
agents is demonstrated.Comment: This is an extended version of an article to appear in the IEEE
Transactions on Automatic Control (additional parts in the Appendix
Uncertain Multi-Agent Systems with Distributed Constrained Optimization Missions and Event-Triggered Communications: Application to Resource Allocation
This paper deals with solving distributed optimization problems with equality
constraints by a class of uncertain nonlinear heterogeneous dynamic multi-agent
systems. It is assumed that each agent with an uncertain dynamic model has
limited information about the main problem and limited access to the
information of the state variables of the other agents. A distributed algorithm
that guarantees cooperative solving of the constrained optimization problem by
the agents is proposed. Via this algorithm, the agents do not need to
continuously broadcast their data. It is shown that the proposed algorithm can
be useful in solving resource allocation problems
Co-design of output feedback laws and event-triggering conditions for linear systems
We present a procedure to simultaneously design the output feedback law and
the event-triggering condition to stabilize linear systems. The closed-loop
system is shown to satisfy a global asymptotic stability property and the
existence of a strictly positive minimum amount of time between two
transmissions is guaranteed. The event-triggered controller is obtained by
solving linear matrix inequalities (LMIs). We then exploit the flexibility of
the method to maximize the guaranteed minimum amount of time between two
transmissions. Finally, we provide a (heuristic) method to reduce the amount of
transmissions, which is supported by numerical simulations
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