Layered Formation-containment Control of Multi-agent Systems in Constrained Space
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This paper addresses the layered formation-containment (LFC) problem for multiagents in the constrained space with a directed communication topology. The formation-containment problem is first defined using a layered framework, and a layered distributed finite-time estimator is proposed to acquire the target states for agents in each layer. Based on the proposed framework, the formation configuration and the mechanism of the information flow can be explored and designed naturally. To avoid collisions with borders, obstacles, as well as the other agents in the constrained space, an artificial potential function is designed based on the Dirac delta function. Further, a disturbance observer and adaptive neural networks (NNs) are applied to respectively tackle the external disturbance and the model uncertainties. The desired formation of each layer can be achieved while no collision occurs in the constrained space. The semi-global uniform ultimate boundedness of closed-loop errors is guaranteed by Lyapunov stability theory. Simulation results are given to show the effectiveness of the proposed approaches.
KeywordsConstrained space formation-containment control multi-agent systems neural networks
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