Asymptotical Feedback Set Stabilization of Probabilistic Boolean Control Networks
Asymptotical Feedback Set Stabilization of Probabilistic Boolean Control Networks
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DOI：
10.1109/tnnls.2019.2955974
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发表时间：
2019-12
影响因子：
10.4
通讯作者：
Rongpei Zhou;Yuqian Guo;Yuhu Wu;W. Gui
中科院分区：
文献类型：
--
作者：
Rongpei Zhou;Yuqian Guo;Yuhu Wu;W. Gui
在本文中，我们研究了概率布尔控制网络（PBCN）分布中的渐近反馈集稳定。我们证明，当且仅当（IFF）构成该子集中包含的最大的控制不变的子集（LCI）时，PBCN在给定子集的渐近反馈均可稳定在给定子集时。我们提出了一种算法，以计算任何给定子集中包含的LCI，并在获得可及性矩阵方面具有必要且充分的条件，以实现渐近集稳定性的必要条件。此外，我们提出了一种基于状态空间分区设计稳定反馈的方法。最后，将结果应用于求解PBCN的渐近反馈输出跟踪和渐近反馈同步。详细说明了示例以证明所提出的方法和结果的可行性。
In this article, we investigate the asymptotical feedback set stabilization in distribution of probabilistic Boolean control networks (PBCNs). We prove that a PBCN is asymptotically feedback stabilizable to a given subset if and only if (iff) it constitutes asymptotically feedback stabilizable to the largest control-invariant subset (LCIS) contained in this subset. We proposed an algorithm to calculate the LCIS contained in any given subset with the necessary and sufficient condition for asymptotical set stabilizability in terms of obtaining the reachability matrix. In addition, we propose a method to design stabilizing feedback based on a state-space partition. Finally, the results were applied to solve asymptotical feedback output tracking and asymptotical feedback synchronization of PBCNs. Examples were detailed to demonstrate the feasibility of the proposed method and results.