This paper provides an overview of the research conducted in the context of structural (or structured) systems. These are parametrized models used to assess and design system theoretical properties without considering a specific realization of the parameters (which could be uncertain or unknown). The research in structural systems led to a principled approach to a variety of problems, into what is known as structural systems theory. Hereafter, we perform a systematic overview of the problems and methodologies used in structural systems theory since the latest survey by Dion et al. in 2003. During this period, most of the focus seems to be on structural system's properties related to controllability/observability and decentralized control, in the context of linear time-invariant systems, under the classic assumption that the parameters are independent and belonging to infinite fields. Notwithstanding, it is notable an increase in research in topics that go beyond such scope and underlying assumptions, as well as applications in a variety of domains. Lastly, we provide a compilation of open questions on several settings and we discuss future directions in this field.@en

Neurotechnology has made great strides in the last 20 years. However, we still have a long way to go to commercialize many of these technologies as we lack a unified framework to study cyber-neural systems (CNS) that bring the hardware, software, and the neural system together. Dynamical systems play a key role in developing these technologies as they capture different aspects of the brain and provide insight into their function. Converging evidence suggests that fractional-order dynamical systems are advantageous in modeling neural systems because of their compact representation and accuracy in capturing the long-range memory exhibited in neural behavior. In this brief survey, we provide an overview of fractional CNS that entails fractional-order systems in the context of CNS. In particular, we introduce basic definitions required for the analysis and synthesis of fractional CNS, encompassing system identification, state estimation, and closed-loop control. Additionally, we provide an illustration of some applications in the context of CNS and draw some possible future research directions. Advancements in these three areas will be critical in developing the next generation of CNS, which will, ultimately, improve people's quality of life.@en

Finding strongly connected components (SCCs) and the diameter of a directed network play a key role in a variety of machine learning and control theory problems. In this article, we provide for the first time a scalable distributed solution for these two problems by leveraging dynamical consensus-like protocols to find the SCCs. The proposed solution has a time complexity of O(NDd in-degreemax), where N is the number of vertices in the network,D is the (finite) diameter of the network, and din-degreemax is the maximum in-degree of the network. Additionally, we prove that our algorithm terminates in D+2 iterations, which allows us to retrieve the finite diameter of the network. We perform exhaustive simulations that support the outperformance of our algorithm against the state of the art on several random networks, including Erdős-Rényi, Barabási-Albert, and Watts-Strogatz networks.@en

We provide a necessary and sufficient graph-theoretical characterization of quotient fixes modes occurring in parametric decentralized control systems. Specifically, we introduce the notion of structurally quotient fixed modes (SQFMs) that generically captures the quotient fixed modes and only depends on the system's structure. Additionally, we provide an efficient polynomial-time algorithm for the verification of this graph-theoretical condition. We show that this algorithm can be parallelized, and linear-time computational complexity approximation algorithms can be considered to attain a sub-optimal solution. Lastly, we discuss the implications of the actuation–sensing–communication capabilities and the systems’ interconnections on the existence of SQFM.@en