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In this paper, we propose a simple framework for closed-loop system identification: the stabilized output error method. Traditional closed-loop system identification methods rely on the linearity of the target system, and they require the identification of noise models or prior knowledge or identifiability of the feedback controllers to obtain unbiased estimates. But in many real-world applications, the noise dynamics and feedback controllers are complex and difficult to identify, and the nonlinearity may not be ignored. The proposed framework introduces a virtual controller that stabilizes the error between model prediction and the output of the target system. This enables us to apply the output error method, which gives unbiased estimates without depending on the noise model and is applicable to a wide range of models, including nonlinear systems, to closed-loop system identification problems. The paper describes the framework and gives the theoretical support and design guidelines for virtual controllers. Through numerical examples, we show the effectiveness of the proposed framework in various situations, which include identification of (a) linear gray box models, (b) systems in the presence of disturbances having realistic complexity, and (c) a nonlinear unstable system in a human-in-the-loop environment.

This paper studies the automated demand response (DR) problem in smart grids equipped with information and communication technology networks, where power generating and consuming units can exchange information as a multiagent system (MAS), and a real-time pricing (RTP) scheme is proposed. When the communication graph among agents is connected, a novel parallel and distributed consensus-based algorithm is proposed to derive an RTP scheme to facilitate DR, and when communication uncertainties exist, a robust consensus algorithm is proposed to cease the effect of uncertainties. Next, this paper proposes a novel control mechanism to tackle the problem of disconnected communication among agents, e.g., under cyber-attacks, by employing the so-called mixed communication-broadcast control architecture where the underlying ideas are twofold. First, each area in the grid associated with a connected subgraph is controlled by a MAS to guarantee the power balance and to reach consensus on the local electric price for that area. Second, a supervisory unit observes those local electric prices to calculate the global electric price for the whole grid and then broadcasts to all units so that they can properly adjust their output powers. Simulations are carried out on the IEEE 39-bus system to validate the proposed control mechanisms.

This paper addresses a distributed formation control problem of multiple mobile agents using relative positions and local bearings. First, a formation control method via distributed pattern matching is proposed, which is executed over each clique (i.e., complete induced subgraph) of a network. It is shown that this method achieves the best control performance for a given desired formation and network topology in the following sense: The closest formation to the desired one is achieved among all formations achievable by distributed and relative control over the network. Next, a necessary and sufficient network condition is derived under which the desired formation can be obtained. It turns out that a new concept of connectivity, called clique rigidity, plays a crucial role. Finally, the effectiveness of the proposed method is illustrated through simulations in both two and three dimensional spaces.

Cactus graphs , which are connected graphs with no edge contained in two or more different simple cycles, are known to provide useful properties for network systems. Meanwhile, it has been found that a noncactus graph can be equivalently transformed into a cactus graph by using a conventional technique for simplifying block diagrams. Based on such graph transformation, we can transform a network system on a general graph (where nodes have deterministic dynamics with a multidimensional state variable) into a network system on a cactus graph while preserving the original dynamics, which allows us to apply existing results for network systems on a cactus graph. However, the condition under which such transformation is possible has never been clarified so far. This paper addresses the problem of finding graphs which can be transformed into a cactus graph. We first introduce the notion of cactus expandability , which corresponds to the existence of a cactus graph equivalent to the original graph. A sufficient condition is presented based on two graph characteristics, called the nucleus graphs and doubly bidirectionally connected pairs . Furthermore, we derive a sufficient condition for the interconnection of graphs. Based on these results, we also present a method to obtain a cactus graph equivalent to the original graph and discuss its computational complexity. We finally apply the results to the robustness analysis of biological networks to demonstrate the potential for systems biology.

Boolean networks are system models whose binary-state nodes are interconnected. Although a number of studies have been conducted so far, it is usually assumed that the full information on the target system is available for analysis and design. This paper addresses a structural analysis problem for Boolean networks, i.e., the case when the information on the network structure is available but that on the node dynamics is unavailable . In particular, we consider here oscillatority (instability). First, the notion of structural oscillatority is formulated based on an equivalence relation of network structures. We next present a necessary and sufficient condition for the so-called cactus Boolean networks to be structurally oscillatory. This condition captures structural oscillatority by a simple characterization in terms of the network structure (more concretely, the number of inhibiting edges in each simple cycles), which enables us to apply it to large-scale Boolean networks.

This paper is concerned with a formation shaping problem for point agents in a two-dimensional space, where control avoids the possibility of reflection ambiguities. One solution for this type of problems was given first for three or four agents by considering a potential function which consists of both the distance error and the signed area terms. Then, by exploiting a hierarchical control strategy with such potential functions, the method was extended to any number of agents recently. However, a specific gain on the signed area term must be employed there, and it does not guarantee the global convergence. To overcome this issue, this paper provides a necessary and sufficient condition for the global convergence, subject to the constraint that the desired formation consists of isosceles triangles only. This clarifies the admissible range of the gain on the signed area for this case. In addition, as for formations consisting of arbitrary triangles, it is shown when high gain on the signed area is admissible for global convergence., Accepted in the 59th IEEE Conference on Decision and Control (CDC 2020). arXiv admin note: text overlap with arXiv:1808.00312

This paper discusses the kernel-based identification which takes account of robustness against input disturbances. To this end, ARX models are employed in the framework of kernel-based system identification. Since ARX models utilize the past output data, which reflects the actual input including the disturbances, as well as the input data, its output estimate is expected to be more accurate compared to conventional FIR models. We will show the Tuned and Correlated kernel, which has been widely used in FIR model identification, works well for ARX models through numerical case studies with real data. Furthermore, results show that the stability of the identified ARX models is eventually maintained and they are superior to FIR models in robustness against input disturbances.