Journal ArticleDOI
Distributed Optimal Coordination for Heterogeneous Linear Multiagent Systems With Event-Triggered Mechanisms
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TLDR
To achieve the DOC for linear multiagent systems with unmeasurable states, an observer-based event-triggered control law is proposed and it is proved that no Zeno behavior is exhibited and the global asymptotic convergence is preserved.Abstract:
This note considers the distributed optimal coordination (DOC) problem for heterogeneous linear multiagent systems. The local gradients are locally Lipschitz and the local convexity constants are unknown. A control law is proposed to drive the states of all agents to the optimal coordination that minimizes a global objective function. By exploring certain features of the invariant projection of the Laplacian matrix, the global asymptotic convergence is guaranteed utilizing only local interaction. The proposed control law is then extended with event-triggered communication schemes, which removes the requirement for continuous communications. Under the event-triggered control law, it is proved that no Zeno behavior is exhibited and the global asymptotic convergence is preserved. The proposed control laws are fully distributed, in the sense that the control design only uses the information in the connected neighborhood. Furthermore, to achieve the DOC for linear multiagent systems with unmeasurable states, an observer-based event-triggered control law is proposed. A simulation example is given to validate the proposed control laws.read more
Citations
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Journal ArticleDOI
Fully-Distributed Deloading Operation of DFIG-Based Wind Farm for Load Sharing
TL;DR: A system-level consensus-based deloading operation is proposed for DFIG-based wind farm (WF) participating in system active power regulation that can fully and quickly extract the available rotor overspeeding margin for a high kinetic energy storage and a better dynamic performance in ROC stage, and minimize the dependency on pitch angle when facing deeper deloaded operation.
Journal ArticleDOI
Design of Distributed Event-Triggered Average Tracking Algorithms for Homogeneous and Heterogeneous Multiagent Systems
TL;DR: In this paper , the design problem of distributed event-triggered average tracking (DETAT) algorithms for homogeneous and heterogeneous multi-agent systems is addressed, and a class of static-gain DETAT algorithms with a couple of local event-related functions for estimators and controllers are proposed.
Journal ArticleDOI
Distributed Model Reference Adaptive Optimization of Disturbed Multiagent Systems With Intermittent Communications
TL;DR: In this article , a distributed model reference adaptive control (D-MRAC) scheme is proposed that no explicit disturbance observer or internal model unit is involved, which not only enhances robustness but also improves transient performance.
Journal ArticleDOI
A new Zeno-free event-triggered scheme for robust distributed optimal coordination
Hao Yu,Tongwen Chen +1 more
TL;DR: It is showed that the robust distributed optimal coordination problem for heterogeneous linear multi-agent systems with external disturbances can be solved practically without Zeno behavior.
Journal ArticleDOI
Distributed Optimal Coordination for Heterogeneous Linear Multiagent Systems
Liwei An,Guang-Hong Yang +1 more
TL;DR: In this paper, a distributed optimal coordination (DOC) algorithm for heterogeneous linear multiagent systems is proposed, which guarantees the global asymptotical convergence by utilizing only local interaction.
References
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TL;DR: In this article, the focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them, and a comprehensive introduction to the subject is given. But the focus of this book is not on the optimization problem itself, but on the problem of finding the appropriate technique to solve it.
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Distributed Subgradient Methods for Multi-Agent Optimization
Angelia Nedic,Asuman Ozdaglar +1 more
TL;DR: The authors' convergence rate results explicitly characterize the tradeoff between a desired accuracy of the generated approximate optimal solutions and the number of iterations needed to achieve the accuracy.
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Constrained Consensus and Optimization in Multi-Agent Networks
TL;DR: In this article, the authors present a distributed algorithm that can be used by multiple agents to align their estimates with a particular value over a network with time-varying connectivity.
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TL;DR: This chapter explains the development of a method for the nonlinear output regulation of singular nonlinear systems and some examples show how this regulation changed from linear to discrete-time systems over time.