控制理论与应用2021，Vol.38Issue(11)：1743-1753,11.DOI:10.7641/CTA.2021.10795
状态翻转控制下布尔控制网络的可镇定性和Q学习算法
State-flipped control and Q-learning algorithm for the stabilization of Boolean control networks
摘要
Abstract
In this paper, the global stabilization of Boolean control networks under state-flipped control with respect to a given subset is addressed. For a given subset of the set of the nodes, the state-flipped control can change the values of some nodes from 1 or 0 to 0 or 1. Considering the flips as controls, Boolean networks under state-flipped control are studied. Combining control inputs with state-flipped controls, the concepts of joint control pair and the state-flipped-transition matrix are proposed. A necessary and sufficient condition is provided to check whether a Boolean control network under state-flipped control can be globally stabilized. An algorithm is developed to find the stabilizing kernel, which is the flip set with the minimal cardinal number. By using the reachable set, another method is provided for global stabilization and joint control pair sequences. Besides, if the system is a large scale network, a model-free reinforcement learning method called Q-learning algorithm, is used for the joint control pair sequences. A numerical example is given to illustrate the theoretical results.关键词
布尔控制网络/半张量积/状态翻转控制/全局镇定性/Q学习算法Key words
Boolean control networks/semi-tensor product/state-flippped control/global stabilization/Q-learning algorithm引用本文复制引用
刘洋,刘泽娇,卢剑权..状态翻转控制下布尔控制网络的可镇定性和Q学习算法[J].控制理论与应用,2021,38(11):1743-1753,11.基金项目
Supported by the National Natural Science Foundation of China(62173308,61973078)and the Natural Science Foundation of Zhejiang Province of China(LR20F030001,LD19A010001). （62173308,61973078）
