Control Node Selection Algorithm for Nonlinear Dynamic Networks
Control Node Selection Algorithm for Nonlinear Dynamic Networks
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DOI：
10.1109/lcsys.2020.3019591
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发表时间：
2020-08
影响因子：
3
通讯作者：
A. Haber;Sebastian A. Nugroho;Patricio Torres;A. Taha
中科院分区：
文献类型：
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作者：
A. Haber;Sebastian A. Nugroho;Patricio Torres;A. Taha
选择控制节点和设计非线性网络动力学控制行为的耦合问题是在许多不同领域都有应用的基本科学问题。这些问题针对线性动力学进行了深入研究。然而，尽管存在许多悬而未决的研究问题，非线性网络动力学的方法还不太发达。正如各种研究所观察到的，用于选择控制节点的流行的基于图的可控性方法可能会导致非线性动力学的控制性能明显次优。我们提出了一种新的、直观的、简单的方法，用于具有非线性动力学的复杂网络的同步控制节点选择和控制序列设计。该方法是通过将控制节点选择问题合并到开环最优控制问题中并使用网格自适应直接搜索方法近似求解所得的混合整数优化问题来开发的。开发的框架在数值上具有鲁棒性，可以处理刚性网络以及控制和执行器约束。通过在原型杜芬振荡器和关联记忆网络上进行测试，证明了该方法良好的数值性能。开发的代码可以很容易地适应其他复杂系统的模型，可以在线获取。
The coupled problems of selecting control nodes and designing control actions for nonlinear network dynamics are fundamental scientific problems with applications in many diverse fields. These problems are thoroughly studied for linear dynamics. However, in spite of a number of open research questions, methods for nonlinear network dynamics are less developed. As observed by various studies, the prevailing graph-based controllability approaches for selecting control nodes might result in significantly suboptimal control performance for nonlinear dynamics. We present a new, intuitive, and simple method for simultaneous control node selection and control sequence design for complex networks with nonlinear dynamics. The method is developed by incorporating the control node selection problem into an open-loop optimal control problem and by approximately solving the resulting mixed-integer optimization problem using a mesh adaptive direct search method. The developed framework is numerically robust and can deal with stiff networks and with control and actuator constraints. Good numerical performance of the method is demonstrated by testing it on prototypical Duffing oscillator and associative memory networks. The developed codes that can easily be adapted to models of other complex systems are available online.