1,746 research outputs found
Distributed Model Predictive Consensus via the Alternating Direction Method of Multipliers
We propose a distributed optimization method for solving a distributed model
predictive consensus problem. The goal is to design a distributed controller
for a network of dynamical systems to optimize a coupled objective function
while respecting state and input constraints. The distributed optimization
method is an augmented Lagrangian method called the Alternating Direction
Method of Multipliers (ADMM), which was introduced in the 1970s but has seen a
recent resurgence in the context of dramatic increases in computing power and
the development of widely available distributed computing platforms. The method
is applied to position and velocity consensus in a network of double
integrators. We find that a few tens of ADMM iterations yield closed-loop
performance near what is achieved by solving the optimization problem
centrally. Furthermore, the use of recent code generation techniques for
solving local subproblems yields fast overall computation times.Comment: 7 pages, 5 figures, 50th Allerton Conference on Communication,
Control, and Computing, Monticello, IL, USA, 201
Resilient and constrained consensus against adversarial attacks: A distributed MPC framework
There has been a growing interest in realizing the resilient consensus of the
multi-agent system (MAS) under cyber-attacks, which aims to achieve the
consensus of normal agents (i.e., agents without attacks) in a network,
depending on the neighboring information. The literature has developed
mean-subsequence-reduced (MSR) algorithms for the MAS with F adversarial
attacks and has shown that the consensus is achieved for the normal agents when
the communication network is at least (2F+1)-robust. However, such a stringent
requirement on the communication network needs to be relaxed to enable more
practical applications. Our objective is, for the first time, to achieve less
stringent conditions on the network, while ensuring the resilient consensus for
the general linear MAS subject to control input constraints. In this work, we
propose a distributed resilient consensus framework, consisting of a
pre-designed consensus protocol and distributed model predictive control (DMPC)
optimization, which can help significantly reduce the requirement on the
network robustness and effectively handle the general linear constrained MAS
under adversarial attacks. By employing a novel distributed adversarial attack
detection mechanism based on the history information broadcast by neighbors and
a convex set (i.e., resilience set), we can evaluate the reliability of
communication links. Moreover, we show that the recursive feasibility of the
associated DMPC optimization problem can be guaranteed. The proposed consensus
protocol features the following properties: 1) by minimizing a group of control
variables, the consensus performance is optimized; 2) the resilient consensus
of the general linear constrained MAS subject to F-locally adversarial attacks
is achieved when the communication network is (F+1)-robust. Finally, numerical
simulation results are presented to verify the theoretical results
A Robust Distributed Model Predictive Control Framework for Consensus of Multi-Agent Systems with Input Constraints and Varying Delays
This paper studies the consensus problem of general linear discrete-time
multi-agent systems (MAS) with input constraints and bounded time-varying
communication delays. We propose a robust distributed model predictive control
(DMPC) consensus protocol that integrates the offline consensus design with
online DMPC optimization to exploit their respective advantages. More
precisely, each agent is equipped with an offline consensus protocol, which is
a priori designed, depending on its immediate neighbors' estimated states.
Further, the estimation errors propagated over time due to inexact neighboring
information are proved bounded under mild technical assumptions, based on which
a robust DMPC strategy is deliberately designed to achieve robust consensus
while satisfying input constraints. Moreover, it is shown that, with the
suitably designed cost function and constraints, the feasibility of the
associated optimization problem can be recursively ensured. We further provide
the consensus convergence result of the constrained MAS in the presence of
bounded varying delays. Finally, two numerical examples are given to verify the
effectiveness of the proposed distributed consensus algorithm
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