69 research outputs found
Scalable {\delta}-Level Coherent State Synchronization of Multi-Agent Systems in the Presence of Bounded Disturbances
In this paper, we study scalable {\delta}-Level coherent state
synchronization for multi-agent systems (MAS) where the agents are subject to
bounded disturbances/noises. We propose a scale-free framework designed solely
based on the knowledge of agent models and agnostic to the communication graphs
and size of the network. We define the level of coherency for each agent as the
norm of the weighted sum of the disagreement dynamics with its neighbors. The
objective is to restrict the level of coherency of the network to {\delta}
without a-priori information about the disturbances.Comment: 18 pages, 11 figures, This is a preprint of the paper "Scalable
{\delta}-Level Coherent State Synchronization of Multi-Agent Systems with
Adaptive Protocols and Bounded Disturbances" submitted to the International
Journal of Robust and Nonlinear Contro
Scale-free Design for Delayed Regulated Synchronization of Homogeneous and Heterogeneous Discrete-time Multi-agent Systems Subject to Unknown Non-uniform and Arbitrarily Large Communication Delays
In this paper, we study delayed regulated state/output synchronization for
discrete-time homogeneous and heterogeneous networks of multi-agent systems
(MAS) subject to unknown, non-uniform and arbitrarily large communication
delays. A delay transformation is utilized to transform the original MAS to a
new system without delayed states. The proposed scale-free dynamic protocols
are developed solely based on agent models and localized information exchange
with neighbors such that we do not need any information about the communication
networks and the number of agents.Comment: 18 pages, 10 figures, a short version of this paper will be submitted
to ACC 2021. arXiv admin note: text overlap with arXiv:2004.09498,
arXiv:2004.1301
Scale-free Protocol Design for Output Synchronization of Heterogeneous Multi-agent subject to Unknown, Non-uniform and Arbitrarily Large Input Delays
This paper studies output synchronization problems for heterogeneous networks
of continuous- or discrete-time right-invertible linear agents in presence of
unknown, non-uniform and arbitrarily large input delay based on localized
information exchange. It is assumed that all the agents are introspective,
meaning that they have access to their own local measurements. Universal linear
protocols are proposed for each agent to achieve output synchronizations.
Proposed protocols are designed solely based on the agent models using no
information about communication graph and the number of agents or other agent
models information. Moreover, the protocols can tolerate arbitrarily large
input delays.Comment: 9 pages, 3 figures, short version of this paper will be presented at
Chinese Control Conference 2020. arXiv admin note: text overlap with
arXiv:2002.06577, arXiv:2001.02117, arXiv:1908.06535, arXiv:2004.0949
Cooperative Control Reconfiguration in Networked Multi-Agent Systems
Development of a network of autonomous cooperating vehicles has attracted significant
attention during the past few years due to its broad range of applications in areas
such as autonomous underwater vehicles for exploring deep sea oceans, satellite formations
for space missions, and mobile robots in industrial sites where human involvement
is impossible or restricted, to name a few. Motivated by the stringent specifications
and requirements for depth, speed, position or attitude of the team and the possibility
of having unexpected actuators and sensors faults in missions for these vehicles have
led to the proposed research in this thesis on cooperative fault-tolerant control design of
autonomous networked vehicles.
First, a multi-agent system under a fixed and undirected network topology and subject
to actuator faults is studied. A reconfigurable control law is proposed and the so-called
distributed Hamilton-Jacobi-Bellman equations for the faulty agents are derived. Then,
the reconfigured controller gains are designed by solving these equations subject to the
faulty agent dynamics as well as the network structural constraints to ensure that the
agents can reach a consensus even in presence of a fault while simultaneously the team
performance index is minimized.
Next, a multi-agent network subject to simultaneous as well as subsequent actuator
faults and under directed fixed topology and subject to bounded energy disturbances is considered. An H∞ performance fault recovery control strategy is proposed that guarantees:
the state consensus errors remain bounded, the output of the faulty system behaves
exactly the same as that of the healthy system, and the specified H∞ performance bound
is guaranteed to be minimized. Towards this end, the reconfigured control law gains
are selected first by employing a geometric control approach where a set of controllers
guarantees that the output of the faulty agent imitates that of the healthy agent and the
consensus achievement objectives are satisfied. Then, the remaining degrees of freedom
in the selection of the control law gains are used to minimize the bound on a specified
H∞ performance index.
Then, control reconfiguration problem in a team subject to directed switching topology
networks as well as actuator faults and their severity estimation uncertainties is considered.
The consensus achievement of the faulty network is transformed into two stability
problems, in which one can be solved offline while the other should be solved online
and by utilizing information that each agent has received from the fault detection and
identification module. Using quadratic and convex hull Lyapunov functions the control
gains are designed and selected such that the team consensus achievement is guaranteed
while the upper bound of the team cost performance index is minimized.
Finally, a team of non-identical agents subject to actuator faults is considered. A
distributed output feedback control strategy is proposed which guarantees that agents
outputs’ follow the outputs of the exo-system and the agents states remains stable even
when agents are subject to different actuator faults
Using MapReduce Streaming for Distributed Life Simulation on the Cloud
Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp
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