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.
Most of the event-triggered approaches have been applied in the context of consensus in MAS @cite_31 @cite_38 @cite_30 . This paper focuses on distributed formation control, which has been considered in @cite_18 @cite_13 @cite_36 . Formation control consists in driving and maintaining all agents of a MAS to some reference, possibly time-varying configuration, defining, @math , their relative positions, orientations, and speeds. Various approaches have been considered, such as behavior-based flocking @cite_17 @cite_39 @cite_32 @cite_40 @cite_44 , or formation tracking @cite_29 @cite_6 @cite_43 @cite_20 @cite_10 .
@cite_30 @cite_38 @cite_18 @cite_36 @cite_10 @cite_29 @cite_32 @cite_6 @cite_39 @cite_44 @cite_43 @cite_40 @cite_31 @cite_13 @cite_20 @cite_17
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This paper is focused on the formation control of multi-agent systems (MAS). A new control law based on event-driven strategies is presented. The agents with any initial position can reach the desired formation and target under such control law. Formability of MAS depends on several key factors: the agents' dynamic structures, the connectivity topology, the properties of the desired formation and the admissible control set. The control actuation updates considered in this paper are event-driven, depending on the ratio of a certain measurement error with respect to the norm of a function of the state. In the study of task coordination for multiagent systems, formation control has received considerable attention due to its potential applications in civil and or military prac-tices. Fundamentally, formation control problem for multiagent systems can be formulated as making a group of agents follow the desired trajectory while maintaining certain prescribed geometric distances among agents. In this paper, we consider the formation control problem for mobile robots with nonlinear dynamics and moving in a 2D environment. To address the inherent challenges due to nonlinear system dynamics and agents' limited sensing communication capabilities, we instill an idea of integrating the recently developed distributed consensus theory into the standard feedback control, and propose a new time-varying cooperative control strategy to solve the formation control problem for multiagent systems. In particular, the proposed design only requires the local and intermittent information exchange among agents to achieve the formation control objective. More importantly, we remove the restriction on the need of the desired trajectory for every agent, and instead design a distributed observer for obtaining the desired trajectory in order to establish the formation in the design. The overall distributed formation control system stability is rigorously proved by using a contraction mapping method under the condition that the sensing communication network among robots is sequentially complete. Simulation is provided to validate the effectiveness of the proposed design. In this paper, we study the problem of reaching a consensus among all the agents in the networked control systems (NCS) in the presence of misbehaving agents. A reputation-based resilient distributed control algorithm is first proposed for the leader-follower consensus network. The proposed algorithm embeds a resilience mechanism that includes four phases (detection, mitigation, identification, and update), into the control process in a distributed manner. At each phase, every agent only uses local and one-hop neighbors' information to identify and isolate the misbehaving agents, and even compensate their effect on the system. We then extend the proposed algorithm to the leaderless consensus network by introducing and adding two recovery schemes (rollback and excitation recovery) into the current framework to guarantee the accurate convergence of the well-behaving agents in NCS. The effectiveness of the proposed method is demonstrated through case studies in multirobot formation control and wireless sensor networks. This thesis addresses the question of planning under uncertainty within a time-dependent changing environment. Original motivation for this work came from the problem of building an autonomous agent able to coordinate with its uncertain environment; this environment being composed of other agents communicating their intentions or non-controllable processes for which some discrete-event model is available. We investigate several approaches for modeling continuous time-dependency in the framework of Markov Decision Processes (MDPs), leading us to a definition of Temporal Markov Decision Problems. Then our approach focuses on two separate paradigms. First, we investigate time-dependent problems as processes and describe them through the formalism of Time-dependent MDPs (TMDPs). We extend the existing results concerning optimality equations and present a new Value Iteration algorithm based on piecewise polynomial function representations in order to solve a more general class of TMDPs. This paves the way to a more general discussion on parametric actions in hybrid state and action spaces MDPs with continuous time. In a second time, we investigate the option of separately modeling the concurrent contributions of exogenous events. This approach of modeling leads to the use of Generalized Semi-Markov Decision Processes (GSMDP). We establish a link between the general framework of Discrete Events Systems Specification (DEVS) and the formalism of GSMDP, allowing us to build sound discrete-event compatible simulators. Then we introduce a simulation-based Policy Iteration approach for explicit-event Temporal Markov Decision Problems. This algorithmic contribution brings together results from simulation theory, forward search in MDPs, and statistical learning theory. The implicit-event approach was tested on a specific version of the Mars rover planning problem and on a drone patrol mission planning problem while the explicit-event approach was evaluated on a subway network control problem.