This note considers the problem of information consensus among multiple agents in the presence of limited and unreliable information exchange with dynamically changing interaction topologies. Both discrete and continuous update schemes are proposed for information consensus. The note shows that information consensus under dynamically changing interaction topologies can be achieved asymptotically if the union of the directed interaction graphs across some time intervals has a spanning tree frequently enough as the system evolves. Simulation results show the effectiveness of our update schemes.

This paper considers the problem of information consensus among multiple agents in the presence of limited and unreliable information exchange with dynamically changing interaction topologies. Both discrete and continuous update schemes are proposed for consensus of information. That the union of a collection of interaction graphs across some time intervals has a spanning tree frequently enough as the system evolves is shown to be a necessary and sufficient condition for information consensus under dynamically changing interaction topologies. Simulation results show the effectiveness of our results.

We consider the control of interacting subsystems whose dynamics and constraints are uncoupled, but whose state vectors are coupled non-separably in a single centralized cost function of a finite horizon optimal control problem. For a given centralized cost structure, we generate distributed optimal control problems for each subsystem and establish that the distributed receding horizon implementation is asymptotically stabilizing. The communication requirements between subsystems with coupling in the cost function are that each subsystem obtain the previous optimal control trajectory of those subsystems at each receding horizon update. The key requirements for stability are that each distributed optimal control not deviate too far from the previous optimal control, and that the receding horizon updates happen su#ciently fast. The theory is applied in simulation for stabilization of a formation of vehicles.

This paper investigates a method for decentralized stabilization of vehicle formations using techniques from algebraic graph theory. The vehicles exchange information according to a pre-specified communication digraph, G. A feedback control is designed using relative information between a vehicle and its inneighbors in G. We prove that a necessary and sufficient condition for an appropriate decentralized linear stabilizing feedback to exist is that G has a rooted directed spanning tree. We show the direct relationship between the rate of convergence to formation and the eigenvalues of the (directed) Laplacian of G. Various special situations are discussed, including symmetric communication graphs and formations with leaders. Several numerical simulations are used to illustrate the results.

In this paper, we presented a review on the current control issues and strategies on a group of unmanned autonomous vehicles/robots formation. Formation control has broad applications and becomes an active research topic in the recent years. In this paper, we attempt to review the key issues in formation control with a focus on the main control strategies for formation control under different kinds of scenarios. Then, we point out some important open questions and the possible future research directions on formation control. This paper contributes with a new and interesting consideration on formation control and its application in distributed parameter systems. We pointed out that formation control should be classified as formation regulation control and formation tracking control, similar to regulator and tracker in conventional control.

and the optimum value of max[P (0)] was max[P (0)] = 00:40844 < 0 which indicates that this system has no robustly unobservable states. For the optimal value of given above, a plot of max[P (t)] as a function of t is shown in Fig. 6.

Abstract. In this paper, we generalize the notion of persistence, which has been originally introduced for two-dimensional formations, to ℜ d for d ≥ 3, seeking to provide a theoretical framework for real world applications, which often are in three-dimensional space as opposed to the plane. We verify that many of the properties of rigid and/or persistent formations established in ℜ 2 are also valid for higher dimensions. Analysing the closed subgraphs and directed paths in persistent graphs, we derive some further properties of persistent formations. We also provide an easily checkable necessary condition for persistence. 1

We consider the control of dynamically decoupled subsystems whose state vectors are coupled in the cost function of a finite horizon optimal control problem. For a given cost structure, we generate distributed optimal control problems for each subsystem and establish that a distributed receding horizon implementation is asymptotically stabilizing. The communication requirements at each receding horizon update include the exchange of the previous optimal control trajectory between subsystems with coupling in the cost function. The key requirements for stability are that each distributed optimal control not deviate too far from the previous one, and that the receding horizon updates happen sufficiently fast. A simulation example of multi-vehicle formation stabilization is provided.

In this paper, we present a novel approach for representing formation structures in terms of queues and formation vertices, rather than with nodes, as well as the introduction of the new concept of artificial potential trenches, for effectively controlling the formation of a group of robots. The scheme improves the scalability and flexibility of robot formations when the team size changes, and at the same time, allows formations to adapt to obstacles. Furthermore, for multirobot teams to operate successfully in real and unstructured environments, the instant goal method is used to effectively solve the local minima problem.

Using directed graphs, we consider consensus seeking problem when the information state of each agent is driven by exogenous inputs, random noise, or nonlinear dynamics. We show conditions under which global dynamic consensus can be achieved and provide boundedness analyses for the inconsistency of the information states between agents when communication noise or inconsistent inputs exist. Simulation studies apply the dynamic consensus seeking concept to a multi-agent coordinated control scenario in the context of the distributed virtual leader/virtual structure approach and the behavioral approach respectively.