No Arabic abstract
It is known that determining the observability and reconstructibility of Boolean control networks (BCNs) are both NP-hard in the number of nodes of BCNs. In this paper, we use the aggregation method to overcome the challenging complexity problem in verifying the observability and reconstructibility of large-scale BCNs with special structures in some sense. First, we define a special class of aggregations that are compatible with observability and reconstructibility (i.e, observability and reconstructibility are meaningful for each part of the aggregation), and show that even for this special class of aggregations, the whole BCN being observable/reconstructible does not imply the resulting sub-BCNs being observable/reconstructible, and vice versa. Second, for acyclic aggregations in this special class, we prove that all resulting sub-BCNs being observable/reconstructible implies the whole BCN being observable/reconstructible. Third, we show that finding such acyclic special aggregations with sufficiently small parts can tremendously reduce computational complexity. Finally, we use the BCN T-cell receptor kinetics model to illustrate the efficiency of these results. In addition, the special aggregation method characterized in this paper can also be used to deal with the observability/reconstructibility of large-scale linear (special classes of nonlinear) control systems with special network structures.
Observabililty is an important topic of Boolean control networks (BCNs). In this paper, we propose a new type of observability named online observability to present the sufficient and necessary condition of determining the initial states of BCNs, when their initial states cannot be reset. And we design an algorithm to decide whether a BCN has the online observability. Moreover, we prove that a BCN is identifiable iff it satisfies controllability and the online observability, which reveals the essence of identification problem of BCNs.
Finite-state systems have applications in systems biology, formal verification and synthesis problems of infinite-state (hybrid) systems, etc. As deterministic finite-state systems, logical control networks (LCNs) consist of a finite number of nodes which can be in a finite number of states and update their states. In this paper, we investigate the synthesis problem for controllability and observability of LCNs by state feedback under the semitensor product framework. We show that state feedback can never enforce controllability of an LCN, but sometimes can enforce its observability. We prove that for an LCN $Sig$ and another LCN $Sig$ obtained by feeding a state-feedback controller into $Sig$, (1) if $Sig$ is controllable, then $Sig$ can be either controllable or not; (2) if $Sig$ is not controllable, then $Sig$ is not controllable either; (3) if $Sig$ is observable, then $Sig$ can be either observable or not; (4) if $Sig$ is not observable, $Sig$ can also be observable or not. We also prove that if an unobservable LCN can be synthesized to be observable by state feedback, then it can also be synthesized to be observable by closed-loop state feedback (i.e., state feedback without any input). Furthermore, we give an upper bound for the number of closed-loop state-feedback controllers that are needed to verify whether an unobservable LCN can be synthesized to be observable by state feedback.
In this paper, we study large-scale networks in terms of observability and controllability. In particular, we compare the number of unmatched nodes in two main types of Scale-Free (SF) networks: the Barab{a}si-Albert (BA) model and the Holme-Kim (HK) model. Comparing the two models based on theory and simulation, we discuss the possible relation between clustering coefficient and the number of unmatched nodes. In this direction, we propose a new algorithm to reduce the number of unmatched nodes via link addition. The results are significant as one can reduce the number of unmatched nodes and therefore number of embedded sensors/actuators in, for example, an IoT network. This may significantly reduce the cost of controlling devices or monitoring cost in large-scale systems.
Large-scale network systems describe a wide class of complex dynamical systems composed of many interacting subsystems. A large number of subsystems and their high-dimensional dynamics often result in highly complex topology and dynamics, which pose challenges to network management and operation. This chapter provides an overview of reduced-order modeling techniques that are developed recently for simplifying complex dynamical networks. In the first part, clustering-based approaches are reviewed, which aim to reduce the network scale, i.e., find a simplified network with a fewer number of nodes. The second part presents structure-preserving methods based on generalized balanced truncation, which can reduce the dynamics of each subsystem.
A new analytical framework consisting of two phenomena: single sample and multiple samples, is proposed to deal with the identification problem of Boolean control networks (BCNs) systematically and comprehensively. Under this framework, the existing works on identification can be categorized as special cases of these two phenomena. Several effective criteria for determining the identifiability and the corresponding identification algorithms are proposed. Three important results are derived: (1) If a BN is observable, it is uniquely identifiable; (2) If a BCN is O1-observable, it is uniquely identifiable, where O1-observability is the most general form of the existing observability terms; (3) A BN or BCN may be identifiable, but not observable. In addition, remarks present some challenging future research and contain a preliminary attempt about how to identify unobservable systems.