This paper addresses the problem of formation control and tracking a of desired trajectory by an Euler-Lagrange multi-agent systems. It is inspired by recent results by and adopts an event-triggered control strategy to reduce the number of communications between agents. For that purpose, to evaluate its control input, each agent maintains estimators of the states of the other agents. Communication is triggered when the discrepancy between the actual state of an agent and the corresponding estimate reaches some threshold. The impact of additive state perturbations on the formation control is studied. A condition for the convergence of the multi-agent system to a stable formation is studied. Simulations show the e ectiveness of the proposed approach.
LBC techniques have been introduced in @cite_7 @cite_42 @cite_14 @cite_28 to reduce the number of communications in trajectory tracking problems. MAS with decoupled nonlinear agent dynamics are considered in @cite_7 @cite_14 . Agents have to follow parametrized paths, designed in a centralized way. CTCs introduced by LBC lead all agents to follow the paths in a synchronized way to set up a desired formation. Communication delays, as well as packet losses are considered. Nevertheless, if input-to-state stability conditions are established, absence of Zeno behavior is not analyzed.
@cite_28 @cite_14 @cite_42 @cite_7
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In this paper, we consider a class of dynamic vehicle routing problems, in which a number of mobile agents in the plane must visit target points generated over time by a stochastic process. It is desired to design motion coordination strategies in order to minimize the expected time between the appearance of a target point and the time it is visited by one of the agents. We propose control strategies that, while making minimal or no assumptions on communications between agents, provide the same level of steady-state performance achieved by the best-known decentralized strategies. In other words, we demonstrate that inter-agent communication does not improve the efficiency of such systems, but merely affects the rate of convergence to the steady state. Furthermore, the proposed strategies do not rely on the knowledge of the details of the underlying stochastic process. Copyright © 2007 John Wiley & Sons, Ltd. We use single-agent and multi-agent Reinforcement Learning (RL) for learning dialogue policies in a resource allocation negotiation scenario. Two agents learn concurrently by interacting with each other without any need for simulated users (SUs) to train against or corpora to learn from. In particular, we compare the Qlearning, Policy Hill-Climbing (PHC) and Win or Learn Fast Policy Hill-Climbing (PHC-WoLF) algorithms, varying the scenario complexity (state space size), the number of training episodes, the learning rate, and the exploration rate. Our results show that generally Q-learning fails to converge whereas PHC and PHC-WoLF always converge and perform similarly. We also show that very high gradually decreasing exploration rates are required for convergence. We conclude that multiagent RL of dialogue policies is a promising alternative to using single-agent RL and SUs or learning directly from corpora. We address the problem of designing decentralized feedback laws to force the outputs of decoupled nonlinear systems (agents) to follow geometric paths while holding a desired formation pattern. To this effect we propose a general framework that takes into account i) the topology of the communication links among the agents, ii) the fact that communications do not occur in a continuous manner, and iii) the cost of exchanging information. We provide conditions under which the resulting overall closed loop system is input-to-state stable and apply the methodology for two cases: agents with nonlinear dynamics in strict feedback form and a class of underactuated vehicles. Furthermore, we address explicitly the case where the communications among the agents occur with non-homogenous, possibly varying delays. A coordinated path-following algorithm is derived for multiple underactuated autonomous underwater vehicles. Simulation results are presented and discussed. In this paper, we investigate distributed multi-agent tracking of a convex set specified by multiple moving leaders with unmeasurable velocities. Various jointly connected interaction topologies of the follower agents with uncertainties are considered in the study of set tracking. Based on the connectivity of the time-varying multi-agent system, necessary and sufficient conditions are obtained for set input-to-state stability and set integral input-to-state stability for a nonlinear neighbor-based coordination rule with switching directed topologies. Conditions for asymptotic set tracking are also proposed with respect to the polytope spanned by the leaders.