This paper investigates the challenging task of identifying switched Boolean networks (SBNs) with unknown initial subnetworks, leveraging the semi-tensor product tool. The inherent complexity arising from the lack of knowledge regarding the initial subnetwork necessitates a rigorous approach to identification. Initially, the observability of SBNs is discussed using a matrix-based method, providing foundational insights for subsequent identification analysis. We find the accurate one-to-one correspondence between states and outputs by taking time into account. Building on this correspondence and the observability property, the identification of state and output evolution rules is systematically addressed, leading to the proposition of a necessary and sufficient condition for successful SBN identification. Subsequently, an efficient algorithm is developed to implement the proposed identification approach. Finally, the theoretical findings are validated through an illustrative example.

Disturbance decoupling is one of the fundamental problems in control systems. This article addresses the original disturbance decoupling problem of singular Boolean networks (SBNs) from a new point of view. The robust solvability of SBNs is discussed by the truth matrix of algebraic constraints first. In order to cope with the multiple solutions, an SBN with disturbances is equivalently transformed into a switched restricted Boolean network (BN). Then utilizing a new approach based on the reduced state transition matrix, the relationship between original disturbance decoupling and robust indistinguishability is revealed, which also builds a link with robust observability. By virtue of a parameter extraction mapping, a verifiable criterion is presented for original disturbance decoupling. Moreover, our approach is still applicable to BNs and of lower computational complexity than the existing results. Finally, illustrative examples are worked out to show the effectiveness of the obtained results.

In this article, the set stabilization problem of Boolean control networks (BCNs) using output-feedback controllers is investigated comprehensively. First, a novel method for stabilizing BCNs, namely, the state dynamics matrix-based method, is proposed. Then, based on the above method, the time-invariant output-feedback (TIOF) and the time-variant output-feedback (TVOF) laws are derived. The necessary and sufficient conditions of both TIOF and TVOF stabilization of BCNs are also given. In addition, corresponding algorithms are provided such that all possible TIOF stabilizers and TVOF stabilizers for given BCNs can be designed. Finally, some illustrative examples are demonstrated, where the results are discussed and the effectiveness of the presented method is validated.