199 research outputs found

    Decentralized Multi-Agent Formation Control with Pole-Region Placement via Cone-Complementarity Linearization

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    [EN] An output-feedback decentralised formation control strategy is pursued under pole-region constraints, assuming that the agents have access to relative position measurements with respect to a set of neighbors in a graph describing the sensing topology. No communication between the agents is assumed; however, a shared one-way communication channel with a pilot is needed for steering tasks. Each agent has a separate copy of the same controller. A virtual structure approach is presented for the formation steering as a whole; actual formation control is established via cone-complementarity linearization algorithms for the appropriate matrix inequalities. In contrast to other research where only stable consensus is pursued, the proposed method allows us to specify settling-time, damping and bandwidth limitations via pole regions. In addition, a full methodology for the decoupled handling of steering and formation control is provided. Simulation results in the example section illustrate the approachThe first author is grateful for the financial support via the grant GVA/2021/082 from Generalitat Valenciana. Part of the authors' research activity in related topics is funded via the grant PID2020-116585GB-I00 through MCIN/AEI/10.13039/501100011033 and by the European Union.González Sorribes, A.; Sala, A.; Armesto, L. (2022). Decentralized Multi-Agent Formation Control with Pole-Region Placement via Cone-Complementarity Linearization. International Journal of Applied Mathematics and Computer Science. 32(3):415-428. https://doi.org/10.34768/amcs-2022-003041542832

    Distributed optimization algorithm for discrete-time heterogeneous multi-agent systems with nonuniform stepsizes

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    This paper is devoted to the distributed optimization problem of heterogeneous multi-agent systems, where the communication topology is jointly strongly connected and the dynamics of each agent is the first-order or second-order integrator. A new distributed algorithm is first designed for each agent based on the local objective function and the local neighbors' information that each agent can access. By a model transformation, the original closed-loop system is converted into a time-varying system and the system matrix of which is a stochastic matrix at any time. Then, by the properties of the stochastic matrix, it is proven that all agents' position states can converge to the optimal solution of a team objective function provided the union communication topology is strongly connected. Finally, the simulation results are provided to verify the effectiveness of the distributed algorithm proposed in this paper

    Consensus Tracking and Containment in Multiagent Networks With State Constraints

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    The ability of tracking is an important prerequisite for multiagent networks to perform collective activities. This article investigates the problem of containment for a weighted multiagent network with continuous-time agents under state constraints. The network is composed of uninformed and informed agents, where the latter receive external inputs. A new general class of distributed nonlinear controllers is designed for accomplishing both containment and consensus tracking, where the state of each agent is required to stay in its desired convex constraint set. We show that, by using matrix analysis, convex analysis, and Lyapunov theory, all agents eventually converge to the convex hull formed by the external inputs while they obey their constraints during the transience. No relationship is assumed between the convex hull and the intersection of all constraint sets. The consensus tracking problem with a single external input is also solved under this framework. As a generalization, we tackle the multiscaled constrained containment problem, where agents can specify their desired buffer zones by either zooming in or zooming out the convex hull. Numerical examples are provided to illustrate the theoretical results
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