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Formation iterative learning control for multi-agent systems with higher-order dynamics

机译:具有高阶动力学的多智能体系统的编队迭代学习控制

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This paper is devoted to solving formation problems of multi-agent systems with higher-order dynamics. By using the iterative learning control (ILC) approaches, effective distributed algorithms are developed to enable all agents in directed graphs to achieve the desired relative formations perfectly over a finite-time interval. It is shown that the graph theory can be combined to develop conditions for both asymptotic stability and monotonic convergence of multi-agent formation ILC. Simulation results are finally given to verify our theoretical study.
机译:本文致力于解决具有高阶动力学的多智能体系统的形成问题。通过使用迭代学习控制(ILC)方法,开发了有效的分布式算法,以使有向图中的所有代理都能在有限的时间间隔内完美地实现所需的相对形式。结果表明,可以结合图论为多智能体形成ILC的渐近稳定性和单调收敛性发展条件。最后给出了仿真结果,以验证我们的理论研究。

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