Asynchronous Distributed Event-Triggered Coordination for Multiagent Coverage Control

Abstract

This paper re-visits a multi-agent deployment problem where agents are restricted from requesting information from other agents as well as sending acknowledgments when information is received. These communication constraints relax the assumptions of instantaneous communication and synchronous actions by agents (request and response actions). In this paper, we propose a fully asynchronous communication aware solution to the multi-agent deployment problem that uses an eventtriggered broadcasting strategy. Unlike all existing triggered solutions, our event-triggered broadcasting algorithm relies on agents to decide when to broadcast (push) information to others in the network without the need for a response from other agents. In addition, the proposed strategy determines how best to move when up-to-date information is unavailable and cannot be requested. The algorithm is capable of achieving similar levels of performance to that of a continuous or periodic strategy. Our solution is proven to achieve asymptotic convergence and simulation results are provided to demonstrate that the proposed event-triggered broadcasting algorithm can achieve an adequate level of performance under the communication constraints.

Event-Triggered Consensus Control for Networked Underactuated Robotic Systems

“…gives that the eigenvalue k + 1 of k g=0 M g is of algebraic multiplicity 1, such that G( k g=0 M g ) contains a spanning tree by Lemma 6. Then, it follows that G( k g=0 M g ) contains a spanning tree by (17). Combining with that k g=0 M g is a row stochastic matrix with positive diagonal elements, we can obtain that k g=0 M g is SIA by Lemma 7.…”

Sliding Mode-Based Robustification of Consensus and Distributed Optimization Control Protocols

“…The nonlinear term m i (x j∈N i ,t) denotes the uncertain agent's dynamics which are allowed to depend on the state of agents in the closed neighborhood of agent i so as to account for possible uncertain couplings between agents, perturbations and parameter uncertainties. Model (4) may represent generators connected to the power system [1], [2], [31], coupled oscillators [31], [32], mobile robots [4], [5], or else. The uncertain term m i (•,t) is not required to be smooth but simply essentially locally bounded.…”

“…6 it is evident that as τ i grows the actual control term "k 4 (•,t)w 4,1 (t)" becomes smoother and smoother. It is also seen that, for t ∈ [2,4), where the uncertainties' magnitude is smaller, the magnitude of the control also reduces in accordance with the time evolution of the uncertainties to be rejected. This, instead, was not the case when the original protocol (7) was adopted.…”

“…During these tests, and accordingly with Fig. 5, we have artificially set to zero the uncertain terms a 4 (t) and b 4 (•,t) of the fourth agent in (25) within the time interval t ∈ [2,4). This has been made to verify that, using the chattering alleviation strategy, the control magnitude becomes smaller when the actual uncertainties and perturbations become smaller as well.…”

“…D istributed control of Multi-Agent Systems (MASs) finds application in many relevant domains such as smart grids control [1], [2], sensor networks [3], multi-robot systems [4], [5], distributed optimization [6], [7].…”