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  • Table of contents
    IEEE Trans. Control Netw. Syst. (IF 4.802) Pub Date : 2019-12-17

    Presents the table of contents for this issue of this publication.

    更新日期:2020-01-04
  • IEEE Transactions on Control of Network Systems
    IEEE Trans. Control Netw. Syst. (IF 4.802) Pub Date : 2019-12-17

    Provides a listing of current staff, committee members and society officers.

    更新日期:2020-01-04
  • Distributed Nonlinear Control Design Using Separable Control Contraction Metrics
    IEEE Trans. Control Netw. Syst. (IF 4.802) Pub Date : 2018-12-05
    Humberto Stein Shiromoto; Max Revay; Ian R. Manchester

    This paper gives convex conditions for the synthesis of a distributed control system for large-scale networked nonlinear dynamic systems. It is shown that the technique of control contraction metrics can be extended to this problem by utilizing separable metric structures, resulting in controllers that only depend on information from local sensors and communications from immediate neighbors. The conditions given are pointwise linear matrix inequalities, and are necessary and sufficient for linear positive systems and certain monotone nonlinear systems. Distributed synthesis methods for systems on chordal graphs are also proposed based on semidefinite program decompositions. The results are illustrated on a problem of vehicle platooning with heterogeneous vehicles, and a network of nonlinear dynamic systems with over 1000 states that is not feedback linearizable and has an uncontrollable linearization.

    更新日期:2020-01-04
  • Controllability and Observability of Boolean Control Networks via Sampled-Data Control
    IEEE Trans. Control Netw. Syst. (IF 4.802) Pub Date : 2018-12-05
    Qunxi Zhu; Yang Liu; Jianquan Lu; Jinde Cao

    In this paper, the controllability and observability of sampled-data Boolean control networks (SDBCNs) are investigated. New phenomena are observed in the study of the controllability and observability of SDBCNs. We routinely convert SDBCNs into linear discrete-time systems by the semitensor product of matrices. Necessary and sufficient conditions are derived for the controllability of SDBCNs. After that, we combine two SDBCNs with the same transition matrix into a new SDBCN to study the observability. Using an iterative algorithm, a stable row vector ${\mathcal {U}}^*$ , called the observability row vector, in finite iterations, is obtained. It is proved that an SDBCN is observable, if and only if $\Vert {\mathcal {U}}^*\Vert _1=N^2-N$ with $N:=2^n$ , where $n$ is the number of state-variables of BNs. Moreover, based on graph theory, a more effective algorithm is given to determine the observability of SDBCNs. Its complexity is not related to the length of the sampling period. In addition, some equivalent necessary and sufficient conditions are put forward for the observability of SDBCNs. Numerical examples are given to demonstrate the effectiveness of the obtained results.

    更新日期:2020-01-04
  • Distributed Orientation Estimation in SO($d$) and Applications to Formation Control and Network Localization
    IEEE Trans. Control Netw. Syst. (IF 4.802) Pub Date : 2018-12-20
    Byung-Hun Lee; Sung-Mo Kang; Hyo-Sung Ahn

    In this paper, we propose a novel distributed orientation estimation strategy, which is based on the measurements of relative orientations of neighbors, in $d$ -dimensional space, where $d \geq 3$ . Since agents do not share a common reference frame, local reference frames are not aligned with each other. Under the proposed orientation estimation law, a rotation matrix that identifies the orientation of a local frame with respect to a common frame is obtained by auxiliary variables. The proposed estimation strategy is applied to formation control and network localization in 3-D space. Since the orientation of each agent is estimated in a global sense, formation control strategy ensures that the formation globally exponentially converges to the desired formation in 3-D space.

    更新日期:2020-01-04
  • Micro Water–Energy Nexus: Optimal Demand-Side Management and Quasi-Convex Hull Relaxation
    IEEE Trans. Control Netw. Syst. (IF 4.802) Pub Date : 2018-12-20
    Qifeng Li; Suhyoun Yu; Ameena S. Al-Sumaiti; Konstantin Turitsyn

    In some countries and regions, water distribution and treatment consume a considerable amount of electric energy. This paper investigates the water network's potential ability to provide demand response services to the power grid for the management of renewable resources under the framework of a distribution-level water–energy nexus (micro WEN). In particular, the hidden controllable water loads, such as irrigation systems, were closely studied as virtual energy storage to improve the flexibility of electrical grids. An optimization model is developed for the demand-side management (DSM) of micro WEN, and the simulation results assert that grid flexibility indeed benefits from taking controllable water loads into account. Although the proposed optimal DSM model is a computationally intractable mixed-integer nonlinear programming (MINLP) problem, quasi-convex hull techniques were developed to relax the MINLP into a mixed-integer convex programming (MICP) problem. The numerical study shows that the quasi-convex hull relaxation is tight and that the resulting MICP problem is computationally efficient.

    更新日期:2020-01-04
  • Consensus of Higher Order Agents: Robustness and Heterogeneity
    IEEE Trans. Control Netw. Syst. (IF 4.802) Pub Date : 2018-12-20
    Dwaipayan Mukherjee; Daniel Zelazo

    This paper explores the use of Kharitonov's Theorem on a class of linear multiagent systems. First, we study a network of the $m$ th order ( $m\geq 2$ ) linear uncertain interval plants and provide conditions for achieving full-state consensus, which relate the stability margins of each agent to the spectrum of the graph Laplacian. Then, a robustness analysis for such systems is presented when an edge weight in the underlying graph is perturbed. The same Kharitonov-based analysis proves useful in a related problem, where heterogeneous higher order linear models of agents are considered in a setup similar to pinning control, and conditions for consensus among the follower agents are derived. Numerous simulation examples validate the results.

    更新日期:2020-01-04
  • The Impact of Information in Distributed Submodular Maximization
    IEEE Trans. Control Netw. Syst. (IF 4.802) Pub Date : 2018-12-20
    David Grimsman; Mohd. Shabbir Ali; João P. Hespanha; Jason R. Marden

    The maximization of submodular functions is an NP-Hard problem for certain subclasses of functions, for which a simple greedy algorithm has been shown to guarantee a solution whose quality is within 1/2 of the optimal. When this algorithm is implemented in a distributed way, agents sequentially make decisions based on the decisions of all previous agents. This work explores how limited access to the decisions of previous agents affects the quality of the solution of the greedy algorithm. Specifically, we provide tight upper and lower bounds on how well the algorithm performs, as a function of the information available to each agent. Intuitively, the results show that performance roughly degrades proportionally to the size of the largest group of agents that make decisions independently. Additionally, we consider the case where a system designer is given a set of agents and a global limit on the amount of information that can be accessed. Our results show that the best designs partition the agents into equally sized sets and allow agents to access the decisions of all previous agents within the same set.

    更新日期:2020-01-04
  • Optimal Induced Spreading of SIS Epidemics in Networks
    IEEE Trans. Control Netw. Syst. (IF 4.802) Pub Date : 2018-12-20
    Zhidong He; Piet Van Mieghem

    Induced spreading aims to maximize the infection probabilities of some target nodes by adjusting the nodal infection rates, which can be applied in biochemical and information spreading. We assume that the adjustment of the nodal infection rates has an associated cost and formulate the induced spreading for susceptible-infected-susceptible (SIS) epidemics in networks as an optimization problem under a constraint on total cost. We address and solve both a static model and a dynamic model for the optimization of the induced SIS spreading. By numerical results in some artificial and real networks, we investigate the effect of the network topology on the optimal induced strategy with a quadratic cost function. In the static method, the infection rate increment on each node is coupled to both the degree and the average hops to the targets. In the dynamic method, we show that the effective resistance could be a good metric to indicate the minimum total cost for targeting a single node. We also illustrate that the minimum total cost increases much more slowly with the increasing fraction of targets in the SIS model than in linear control systems.

    更新日期:2020-01-04
  • Distributed Optimization for Network Resource Allocation With Nonsmooth Utility Functions
    IEEE Trans. Control Netw. Syst. (IF 4.802) Pub Date : 2018-12-20
    Hideaki Iiduka

    The network utility maximization problem is the problem of maximizing the overall utility of a network under capacity constraints, where each source in the network has its own private nonsmooth concave utility function (which allows the true utility to be modeled accurately) and each link in the network has only its capacity constraint. To solve this problem, two distributed optimization algorithms are proposed: a projected proximal algorithm and a projected subgradient algorithm. These algorithms can be implemented for the case that each source tries to maximize only its utility by using its proximity operator or subdifferential and each link tries to satisfy only its capacity constraint by using the metric projection onto its capacity constraint set. A convergence analysis indicates that these algorithms are sufficient for each source to find the optimal resource allocation. The convergence, optimality, and performance of the proposed algorithms are demonstrated through numerical comparisons with the existing decentralized network flow control algorithm.

    更新日期:2020-01-04
  • Explicit Computation of Sampling Period in Periodic Event-Triggered Multiagent Control Under Limited Data Rate
    IEEE Trans. Control Netw. Syst. (IF 4.802) Pub Date : 2018-12-20
    Pian Yu; Dimos V. Dimarogonas

    This paper investigates the coordination of nonlinear sampled-data multiagent systems subject to a data rate constraint. The purpose is to design resource-efficient communication and control strategies that guarantee exponential synchronization. Two implementation scenarios are considered: the period time-triggered control and the period event-triggered control. One of the main difficulties of the problem is to obtain an explicit formula for the maximum-allowable sampling period (MASP). To this end, an approach on finding the MASP for period time-triggered control is proposed first. Then, an asynchronous period event-triggered control strategy is formulated, and a communication function and a control function are designed for each agent to determine, respectively, whether the sampled state and control input should be transmitted at each sampling instant. Finally, the constraint of the limited data rate is considered. An observer-based encoder–decoder and a finite-level quantizer are designed, respectively, for the sensor-controller communication and the controller-actuator communication such that a certain constraint on the data rate is satisfied. It is shown that exponential synchronization can still be achieved in the presence of the data-rate constraint. A simulation example is given to illustrate the effectiveness of the theoretical results.

    更新日期:2020-01-04
  • Monostability and Bistability of Boolean Networks Using Semitensor Products
    IEEE Trans. Control Netw. Syst. (IF 4.802) Pub Date : 2018-12-20
    Shuqi Chen; Yuhu Wu; Matthew Macauley; Xi-Ming Sun

    This paper investigates the monostability and bistability of Boolean networks using semitensor products (STPs). A theorem characterizing the stability of a Boolean network is presented and then used to develop three new algorithms. The first determines the stability in $O(2^n)$ , a marked improvement of the complexity of $O((2^n-1)2^{2.81n})$ , the results from simply using Strassen's recursive algorithm. The second algorithm computes the transient period of a given system state, and the last one maximizes this over an entire Boolean network. We conclude by applying these algorithms to two published Boolean models of well-known biological networks in E. coli .

    更新日期:2020-01-04
  • Robust Power System State Estimation From Rank-One Measurements
    IEEE Trans. Control Netw. Syst. (IF 4.802) Pub Date : 2019-01-04
    Gang Wang; Hao Zhu; Georgios B. Giannakis; Jian Sun

    The unique features of current and upcoming energy systems, namely, high penetration of uncertain renewables, unpredictable customer participation, and purposeful manipulation of meter readings, all highlight the need for fast and robust power system state estimation (PSSE). In the absence of noise, PSSE is equivalent to solving a system of quadratic equations, which, also related to power flow analysis, is NP-hard in general. Assuming the availability of all power flow and voltage magnitude measurements, this paper first suggests a simple algebraic technique to transform the power flows into rank-one measurements, for which the $\ell _1$ -based misfit is minimized. To uniquely cope with the nonconvexity and nonsmoothness of $\ell _1$ -based PSSE, a deterministic proximal-linear solver is developed based on composite optimization, whose generalization using stochastic gradients is discussed too. This paper also develops conditions on the $\ell _1$ -based loss function such that exact recovery and quadratic convergence of the proposed scheme are guaranteed. Simulated tests using several IEEE benchmark test systems under different settings corroborate our theoretical findings, as well as the fast convergence and robustness of the proposed approaches.

    更新日期:2020-01-04
  • Top-Down Synthesis of Multiagent Formation Control: An Eigenstructure Assignment Based Approach
    IEEE Trans. Control Netw. Syst. (IF 4.802) Pub Date : 2019-01-04
    Takatoshi Motoyama; Kai Cai

    We propose a top-down approach for formation control of heterogeneous multiagent systems, based on the method of eigenstructure assignment . Given the problem of achieving scalable formations on the plane, our approach globally computes a state feedback control that assigns desired closed-loop eigenvalues/eigenvectors. We characterize the relation between the eigenvalues/eigenvectors and the resulting interagent communication topology, and design special (sparse) topologies such that the synthesized control may be implemented locally by the individual agents. Moreover, we present a hierarchical synthesis procedure that significantly improves computational efficiency. Finally, we extend the proposed approach to achieve fixed-size formation and circular motion, and illustrate these results by simulation examples.

    更新日期:2020-01-04
  • Online Leader Selection for Collective Tracking and Formation Control: The Second-Order Case
    IEEE Trans. Control Netw. Syst. (IF 4.802) Pub Date : 2019-01-04
    Antonio Franchi; Paolo Robuffo Giordano; Giulia Michieletto

    In this paper, we deal with a double control task for a group of interacting agents that have second-order dynamics. Adopting the leader–follower paradigm, the given multiagent system is required to maintain a desired formation and to collectively track a velocity reference provided by an external source only to a single agent at time, called the “leader.” We prove that it is possible to optimize the group performance by persistently selecting online the leader among the agents. To do this, we first define a suitable error metric that is able to capture the tracking performance of the multiagent group while maintaining a desired formation through a (even time-varying) communication-graph topology. Then, we show that this depends on the algebraic connectivity and on the maximum eigenvalue of the Laplacian matrix of a special directed graph depending on the selected leader. By exploiting these theoretical results, we finally design a fully distributed adaptive procedure that is able to periodically select online the optimum leader among the neighbors of the current one. The effectiveness of the proposed solution against other possible strategies is confirmed by numerical simulations.

    更新日期:2020-01-04
  • Distributed Robust Global Containment Control of Second-Order Multiagent Systems With Input Saturation
    IEEE Trans. Control Netw. Syst. (IF 4.802) Pub Date : 2019-01-17
    Junjie Fu; Ying Wan; Guanghui Wen; Tingwen Huang

    In this paper, we study the robust global containment control problem for second-order multiagent systems with bounded input disturbances subject to input saturation under general directed communication graphs. Two types of distributed controllers based on novel sliding mode control ideas are respectively proposed to solve the globally asymptotic containment problem for multiagent systems with static leaders and the practical containment problem for the case with dynamic leaders subject to unknown control inputs. One distinctive feature of the proposed controllers is that only local velocity measurements, relative position, and velocity measurements are involved in designing the controllers, which thus effectively reduces the information transmission burden among the agents for real-time implementation. Another favorable property of the designed controllers is that the global information such as the spectrum of the graph Laplacian matrix is not required in designing these controllers under general directed communication graphs. Simulations on containment control of multiple quadrotors are performed to illustrate the effectiveness of the proposed containment controllers.

    更新日期:2020-01-04
  • POSE.3C: Prediction-Based Opportunistic Sensing Using Distributed Classification, Clustering, and Control in Heterogeneous Sensor Networks
    IEEE Trans. Control Netw. Syst. (IF 4.802) Pub Date : 2019-02-01
    James Zachary Hare; Shalabh Gupta; Thomas A. Wettergren

    This paper presents a distributed algorithm, called prediction-based opportunistic sensing using distributed classification, clustering, and control (POSE.3C), for self adaptation of sensor networks for energy management. The underlying 3C network autonomy concept enables utilization of the target classification information to form dynamic clusters around the predicted target position via selection of sensor nodes with the highest energies and maximum geometric diversity. Furthermore, the nodes can probabilistically control their heterogeneous devices to track targets of interest and minimize energy consumption in a completely distributed manner. Theoretical properties of the POSE.3C network are established and derived in terms of the network lifetime and missed detection characteristics. The algorithm is validated through extensive simulations that demonstrate a significant increase in the network lifetime as compared to other network control approaches, while providing high tracking accuracy and low missed detection rates.

    更新日期:2020-01-04
  • On Mean Field Games for Agents With Langevin Dynamics
    IEEE Trans. Control Netw. Syst. (IF 4.802) Pub Date : 2019-02-01
    Kaivalya Bakshi; Piyush Grover; Evangelos A. Theodorou

    Mean field games (MFG) have emerged as a viable tool in the analysis of large-scale self-organizing networked systems. In particular, MFGs provide a game-theoretic optimal control interpretation of the emergent behavior of noncooperative agents. The purpose of this paper is to study MFG models in which individual agents obey multidimensional nonlinear Langevin dynamics, and analyze the closed-loop stability of fixed points of the corresponding coupled forward-backward partial differential equation (PDE) systems. In such MFG models, the detailed balance property of the reversible diffusions underlies the perturbation dynamics of the forward–backward system. We use our approach to analyze closed-loop stability of two specific models. Explicit control design constraints, which guarantee stability, are obtained for a population distribution model and a mean consensus model. We also show that static state feedback using the steady-state controller can be employed to locally stabilize an MFG equilibrium.

    更新日期:2020-01-04
  • Game-Theoretic Vaccination Against Networked SIS Epidemics and Impacts of Human Decision-Making
    IEEE Trans. Control Netw. Syst. (IF 4.802) Pub Date : 2019-02-06
    Ashish R. Hota; Shreyas Sundaram

    In this paper, we study decentralized protection strategies against susceptible-infected-susceptible epidemics on networks. We consider a population game framework where nodes choose whether or not to vaccinate themselves, and the epidemic risk is defined as the infection probability at the endemic state of the epidemic under a degree-based mean-field approximation. Motivated by studies in behavioral economics showing that humans perceive probabilities and risks in a nonlinear fashion, we specifically examine the impacts of such misperceptions on the Nash equilibrium protection strategies. We first establish the existence and uniqueness of a threshold equilibrium where nodes with degrees larger than a certain threshold vaccinate. When the vaccination cost is sufficiently high, we show that behavioral biases cause fewer players to vaccinate, and vice versa. We quantify this effect for a class of networks with power-law degree distributions by proving tight bounds on the ratio of equilibrium thresholds under behavioral and true perceptions of probabilities. We further characterize the socially optimal vaccination policy and investigate the inefficiency of Nash equilibrium.

    更新日期:2020-01-04
  • Sensor Network Event Localization via Nonconvex Nonsmooth ADMM and Augmented Lagrangian Methods
    IEEE Trans. Control Netw. Syst. (IF 4.802) Pub Date : 2019-02-06
    Chunlei Zhang; Yongqiang Wang

    Event localization plays a fundamental role in many wireless-sensor network applications, such as environmental monitoring, homeland security, medical treatment, and health care, and it is essentially a nonconvex and nonsmooth problem. In this paper, we address such a problem in a completely decentralized way based on augmented Lagrangian methods and alternating direction method of multipliers (ADMM). A decentralized algorithm is proposed to solve the nonsmooth and nonconvex event localization problem directly, rather than using conventional convex relaxation techniques. The avoidance of convex relaxation is significant in that convex relaxation-based methods generally suffer from high computational complexity. The convergence properties are also evaluated and substantiated using numerical simulations, which show that the proposed algorithm achieves better localization accuracy than existing projection-based approaches when the target is within the convex hull of localization sensors. When the target is outside the convex hull, numerical simulations show that the proposed approach has a higher probability to converge to the target event location than existing projection-based approaches. Numerical simulation results show that our approach is also robust to network topology changes.

    更新日期:2020-01-04
  • A Feedback Control Algorithm to Steer Networks to a Cournot–Nash Equilibrium
    IEEE Trans. Control Netw. Syst. (IF 4.802) Pub Date : 2019-02-06
    Claudio De Persis; Nima Monshizadeh

    In this paper, we propose distributed feedback control that steers a dynamical network to a prescribed equilibrium corresponding to the so-called Cournot–Nash equilibrium. The network dynamics considered here are a class of passive nonlinear second-order systems, where production and demands act as external inputs to the systems. While productions are assumed to be controllable at each node, the demand is determined as a function of local prices according to the utility of the consumers. Using reduced information on the demand, the proposed controller guarantees the convergence of the closed-loop system to the optimal equilibrium point dictated by the Cournot–Nash competition.

    更新日期:2020-01-04
  • Information for authors
    IEEE Trans. Control Netw. Syst. (IF 4.802) Pub Date : 2019-12-17

    Provides instructions and guidelines to prospective authors who wish to submit manuscripts.

    更新日期:2020-01-04
  • Control Analysis and Design for Statistical Models of Spiking Networks.
    IEEE Trans. Control Netw. Syst. (IF 4.802) Pub Date : 2019-04-16
    Anirban Nandi,MohammadMehdi Kafashan,ShiNung Ching

    A popular approach to characterizing activity in neuronal networks is the use of statistical models that describe neurons in terms of their firing rates (i.e., the number of spikes produced per unit time). The output realization of a statistical model is, in essence, an n-dimensional binary time series, or pattern. While such models are commonly fit to data, they can also be postulated de novo, as a theoretical description of a given spiking network. More generally, they can model any network producing binary events as a function of time. In this paper, we rigorously develop a set of analyses that may be used to assay the controllability of a particular statistical spiking model, the point-process generalized linear model (PPGLM). Our analysis quantifies the ease or difficulty of inducing desired spiking patterns via an extrinsic input signal, thus providing a framework for basic network analysis, as well as for emerging applications such as neurostimulation design.

    更新日期:2019-11-01
  • State observation and sensor selection for nonlinear networks.
    IEEE Trans. Control Netw. Syst. (IF 4.802) Pub Date : 2018-10-16
    Aleksandar Haber,Ferenc Molnar,Adilson E Motter

    A large variety of dynamical systems, such as chemical and biomolecular systems, can be seen as networks of nonlinear entities. Prediction, control, and identification of such nonlinear networks require knowledge of the state of the system. However, network states are usually unknown, and only a fraction of the state variables are directly measurable. The observability problem concerns reconstructing the network state from this limited information. Here, we propose a general optimization-based approach for observing the states of nonlinear networks and for optimally selecting the observed variables. Our results reveal several fundamental limitations in network observability, such as the trade-off between the fraction of observed variables and the observation length on one side, and the estimation error on the other side. We also show that, owing to the crucial role played by the dynamics, purely graph-theoretic observability approaches cannot provide conclusions about one's practical ability to estimate the states. We demonstrate the effectiveness of our methods by finding the key components in biological and combustion reaction networks from which we determine the full system state. Our results can lead to the design of novel sensing principles that can greatly advance prediction and control of the dynamics of such networks.

    更新日期:2019-11-01
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