2,679 research outputs found
Distributed Robust Set-Invariance for Interconnected Linear Systems
We introduce a class of distributed control policies for networks of
discrete-time linear systems with polytopic additive disturbances. The
objective is to restrict the network-level state and controls to user-specified
polyhedral sets for all times. This problem arises in many safety-critical
applications. We consider two problems. First, given a communication graph
characterizing the structure of the information flow in the network, we find
the optimal distributed control policy by solving a single linear program.
Second, we find the sparsest communication graph required for the existence of
a distributed invariance-inducing control policy. Illustrative examples,
including one on platooning, are presented.Comment: 8 Pages. Submitted to American Control Conference (ACC), 201
Distributed Design for Decentralized Control using Chordal Decomposition and ADMM
We propose a distributed design method for decentralized control by
exploiting the underlying sparsity properties of the problem. Our method is
based on chordal decomposition of sparse block matrices and the alternating
direction method of multipliers (ADMM). We first apply a classical
parameterization technique to restrict the optimal decentralized control into a
convex problem that inherits the sparsity pattern of the original problem. The
parameterization relies on a notion of strongly decentralized stabilization,
and sufficient conditions are discussed to guarantee this notion. Then, chordal
decomposition allows us to decompose the convex restriction into a problem with
partially coupled constraints, and the framework of ADMM enables us to solve
the decomposed problem in a distributed fashion. Consequently, the subsystems
only need to share their model data with their direct neighbours, not needing a
central computation. Numerical experiments demonstrate the effectiveness of the
proposed method.Comment: 11 pages, 8 figures. Accepted for publication in the IEEE
Transactions on Control of Network System
Decentralized Control of Large Collaborative Swarms using Random Finite Set Theory
Controlling large swarms of robotic agents presents many challenges
including, but not limited to, computational complexity due to a large number
of agents, uncertainty in the functionality of each agent in the swarm, and
uncertainty in the swarm's configuration. The contribution of this work is to
decentralize Random Finite Set (RFS) control of large collaborative swarms for
control of individual agents. The RFS control formulation assumes that the
topology underlying the swarm control is complete and uses the complete graph
in a centralized manner. To generalize the control topology in a localized or
decentralized manner, sparse LQR is used to sparsify the RFS control gain
matrix obtained using iterative LQR. This allows agents to use information of
agents near each other (localized topology) or only the agent's own information
(decentralized topology) to make a control decision. Sparsity and performance
for decentralized RFS control are compared for different degrees of
localization in feedback control gains which show that the stability and
performance compared to centralized control do not degrade significantly in
providing RFS control for large collaborative swarms.Comment: arXiv admin note: text overlap with arXiv:1810.0069
Sparse Signal Processing Concepts for Efficient 5G System Design
As it becomes increasingly apparent that 4G will not be able to meet the
emerging demands of future mobile communication systems, the question what
could make up a 5G system, what are the crucial challenges and what are the key
drivers is part of intensive, ongoing discussions. Partly due to the advent of
compressive sensing, methods that can optimally exploit sparsity in signals
have received tremendous attention in recent years. In this paper we will
describe a variety of scenarios in which signal sparsity arises naturally in 5G
wireless systems. Signal sparsity and the associated rich collection of tools
and algorithms will thus be a viable source for innovation in 5G wireless
system design. We will discribe applications of this sparse signal processing
paradigm in MIMO random access, cloud radio access networks, compressive
channel-source network coding, and embedded security. We will also emphasize
important open problem that may arise in 5G system design, for which sparsity
will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces
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