1,442 research outputs found
Event-triggered leader-following formation control of general linear multi-agent systems with distributed infinite input time delays
By employing event-triggered control technique, this paper investigates the leaderfollowing formation control problem of general linear multi-agent systems with distributed infinite input time delays. To decrease computing costs, a novel event-triggered formation protocol taking into consideration of the distributed infinite time delays between agents is put forward. Under the designed triggering function and triggering condition, a sufficient condition on leader-following formation is obtained, and then the Zeno-behavior of triggering time sequences is excluded for the concerned closed-loop system. The continuous update of controller for each agent is avoided. Finally, the correctness and the effectiveness of these theoretical results are demonstrated by two numerical examples
Synchronization of multiple rigid body systems: a survey
The multi-agent system has been a hot topic in the past few decades owing to
its lower cost, higher robustness, and higher flexibility. As a particular
multi-agent system, the multiple rigid body system received a growing interest
since its wide applications in transportation, aerospace, and ocean
exploration. Due to the non-Euclidean configuration space of attitudes and the
inherent nonlinearity of the dynamics of rigid body systems, synchronization of
multiple rigid body systems is quite challenging. This paper aims to present an
overview of the recent progress in synchronization of multiple rigid body
systems from the view of two fundamental problems. The first problem focuses on
attitude synchronization, while the second one focuses on cooperative motion
control in that rotation and translation dynamics are coupled. Finally, a
summary and future directions are given in the conclusion
Event-based H∞ consensus control of multi-agent systems with relative output feedback: The finite-horizon case
In this technical note, the H∞ consensus control problem is investigated over a finite horizon for general discrete time-varying multi-agent systems subject to energy-bounded external disturbances. A decentralized estimation-based output feedback control protocol is put forward via the relative output measurements. A novel event-based mechanism is proposed for each intelligent agent to utilize the available information in order to decide when to broadcast messages and update control input. The aim of the problem addressed is to co-design the time-varying controller and estimator parameters such that the controlled multi-agent systems achieve consensus with a disturbance attenuation level γ over a finite horizon [0,T]. A constrained recursive Riccati difference equation approach is developed to derive the sufficient conditions under which the H∞ consensus performance is guaranteed in the framework of event-based scheme. Furthermore, the desired controller and estimator parameters can be iteratively computed by resorting to the Moore-Penrose pseudo inverse. Finally, the effectiveness of the developed event-based H∞ consensus control strategy is demonstrated in the numerical simulation
Periodic event-triggered output regulation for linear multi-agent systems
This study considers the problem of periodic event-triggered (PET)
cooperative output regulation for a class of linear multi-agent systems. The
advantage of the PET output regulation is that the data transmission and
triggered condition are only needed to be monitored at discrete sampling
instants. It is assumed that only a small number of agents can have access to
the system matrix and states of the leader. Meanwhile, the PET mechanism is
considered not only in the communication between various agents, but also in
the sensor-to-controller and controller-to-actuator transmission channels for
each agent. The above problem set-up will bring some challenges to the
controller design and stability analysis. Based on a novel PET distributed
observer, a PET dynamic output feedback control method is developed for each
follower. Compared with the existing works, our method can naturally exclude
the Zeno behavior, and the inter-event time becomes multiples of the sampling
period. Furthermore, for every follower, the minimum inter-event time can be
determined \textit{a prior}, and computed directly without the knowledge of the
leader information. An example is given to verify and illustrate the
effectiveness of the new design scheme.Comment: 17 pages, 13 figures, submitted to Automatica. accepte
Self-triggered Consensus Control of Multi-agent Systems from Data
This paper considers self-triggered consensus control of unknown linear
multi-agent systems (MASs). Self-triggering mechanisms (STMs) are widely used
in MASs, thanks to their advantages in avoiding continuous monitoring and
saving computing and communication resources. However, existing results require
the knowledge of system matrices, which are difficult to obtain in real-world
settings. To address this challenge, we present a data-driven approach to
designing STMs for unknown MASs building upon the model-based solutions. Our
approach leverages a system lifting method, which allows us to derive a
data-driven representation for the MAS. Subsequently, a data-driven
self-triggered consensus control (STC) scheme is designed, which combines a
data-driven STM with a state feedback control law. We establish a data-based
stability criterion for asymptotic consensus of the closed-loop MAS in terms of
linear matrix inequalities, whose solution provides a matrix for the STM as
well as a stabilizing controller gain. In the presence of external
disturbances, a model-based STC scheme is put forth for
-consensus of MASs, serving as a baseline for the
data-driven STC. Numerical tests are conducted to validate the correctness of
the data- and model-based STC approaches. Our data-driven approach demonstrates
a superior trade-off between control performance and communication efficiency
from finite, noisy data relative to the system identification-based one
A Survey of Resilient Coordination for Cyber-Physical Systems Against Malicious Attacks
Cyber-physical systems (CPSs) facilitate the integration of physical entities
and cyber infrastructures through the utilization of pervasive computational
resources and communication units, leading to improved efficiency, automation,
and practical viability in both academia and industry. Due to its openness and
distributed characteristics, a critical issue prevalent in CPSs is to guarantee
resilience in presence of malicious attacks. This paper conducts a
comprehensive survey of recent advances on resilient coordination for CPSs.
Different from existing survey papers, we focus on the node injection attack
and propose a novel taxonomy according to the multi-layered framework of CPS.
Furthermore, miscellaneous resilient coordination problems are discussed in
this survey. Specifically, some preliminaries and the fundamental problem
settings are given at the beginning. Subsequently, based on a multi-layered
framework of CPSs, promising results of resilient consensus are classified and
reviewed from three perspectives: physical structure, communication mechanism,
and network topology. Next, two typical application scenarios, i.e.,
multi-robot systems and smart grids are exemplified to extend resilient
consensus to other coordination tasks. Particularly, we examine resilient
containment and resilient distributed optimization problems, both of which
demonstrate the applicability of resilient coordination approaches. Finally,
potential avenues are highlighted for future research.Comment: 35 pages, 7 figures, 5 table
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