Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4947769 | Neurocomputing | 2017 | 9 Pages |
Abstract
This paper investigates the time-optimal state-feedback stabilization of switched Boolean control networks (SBCNs). Based on the properties of the semi-tensor product (STP) of matrices, a technique is proposed to merge the switching signal and all inputs of subnetworks into a single input variable. This technique allows us to transfer a SBCN to an equivalent non-switching logical control network (LCN) and eases the analysis and design process significantly. So long as a state-feedback stabilizer is obtained for the resulting non-switching LCN, it can then be decomposed uniquely into the respective state feedbacks of the sub-networks and the state-dependent switching law. Based on this technique, the controllability and stabilisability of such SBCNs are solved and an algorithm for finding all time-optimal switching state feedbacks is proposed. An example is described to illustrate the main results and the design process proposed in this paper.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Yong Ding, Yuqian Guo, Yongfang Xie, Chunhua Yang, Weihua Gui,