Adaptive Neural Finite-Time Deployment of Nonlinear Heterogeneous Multi-Agent Systems With Inconsistent Semi-Markov Topologies: An ODE–PDE Approach
研究了一类大规模异构非线性多智能体系统的有限时间空间部署问题，提出了一种基于常微分方程和偏微分方程的混合分析方法，并设计了自适应神经控制方案来保证跟踪误差的有限时间稳定性。
This article investigates the practical finite-time spatial deployment of a class of large-scale heterogeneous nonlinear multi-agent systems (MASs), for which a novel hybrid analysis methodology based on ordinary differential equations (ODEs) coupled with partial differential equations (PDEs) is proposed. The assumption is made that a portion of the agents is sparsely distributed in space, while the other portion is densely distributed. By designing appropriate network communication protocols (NCPs), the dynamics of MASs are represented by a hybrid model consisting of several ODEs and a PDE. Particularly, the network topological weights are specifically designed as semi-Markov switched to better align with real communication situations of MASs, while complying with inconsistent switching rules. Moreover, for delay-free and time-delayed cases, this article proposes two novel projection-based adaptive neural control schemes and obtains two design criteria of controller gains, such that the practical finite-time stability of the tracking error systems could be guaranteed. Finally, numerical examples are provided to illustrate the effectiveness of the developed approaches.