Search

Adaptive control strategies have been developed in response to more advanced complex systems and to deal with uncertain systems while maintaining the desired conditions. This paper addresses the networked unknown and unstable heterogeneous systems following a stable reference (leader), which is related to network synchronization. We deliver two different scenarios; each agent both fully communicates to the leader and shares communication among neighborhood agents and the leader. The communication among agents and the leader are weighted using Laplacian-like matrix and the model weight matrix in turn. Also, the state and input delays are induced to the systems to capture the real limited communication while the prediction of the reference signals and the augmented systems are proposed to deal with them. Moreover, the rigorous mathematical foundations of two adaptive laws, the stability analysis, the threshold of network, and the communication network are thoroughly presented. Also, the numerical illustrations of the two scenarios are given to show the effectiveness of the proposed method in the networked system. The results show that for both scenarios working on the required setting, the perfect tracking to the leader is guaranteed. Beyond that, the future research would implement the distributed adaptive control-oriented learning of networked system under some faults. [ABSTRACT FROM AUTHOR]

This paper deals with a recursive filtering problem for a class of discrete time-varying nonlinear networked systems with the encoding-decoding mechanism. The linear fitting method is introduced to handle the nonlinearity. An encoding-decoding mechanism is constructed to describe the data transmission process in wireless communication networks (WCNs). To be specific, the measurement outputs are mapped by a quantizer to unique codewords for transmission in WCNs. Then, the codewords are decoded by the decoder to recover the measurement outputs which are sent to the filter. The processing/encoding delay and network delay have been considered. Firstly, on the premise that the upper bound of the filtering error covariance is minimum, the appropriate filtering gain is calculated. Then, the mean square exponential boundedness of the filtering error is analyzed. Finally, two simulation examples are presented to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the H∞$$ {H}_{\infty } $$ saturated control problems of networked systems with random sampling periods and stochastic transmission delays are investigated. The main issue is to design an H∞$$ {H}_{\infty } $$ saturated controller such that the controlled close‐loop system is exponentially mean‐square stable with a desired H∞$$ {H}_{\infty } $$ performance. A matrix exponential which is different from some existing literature is used to reconstruct the discrete‐time stochastic system to an equivalent yet tractable augmented model without any additional conditional constraint. In virtue of reduced‐order confluent Vandermonde matrix approach and Kronecker product operation, the stability condition in terms of linear matrix inequality is derived and then the desired H∞$$ {H}_{\infty } $$ saturated controller is designed. It should be emphasized that the improved method can also be used for the stability analysis and controller design when the system matrix contains complex roots. Finally, two numerical examples and a three‐axis milling machine system are utilized to manifest the reliability and practicality of the designed strategy. [ABSTRACT FROM AUTHOR]

This paper concerns with the problem of fault detection for networked fuzzy systems. Considering the digital communication channel, quantization is adopted to make the data transmission feasible and the induced data distortion is converted into a bounded uncertain term. For the purpose of saving network resources, a new quantizer with an event-triggered strategy is designed to reduce the length and the number of the transmitted data packets, simultaneously. In the process of stability analysis, the T-S fuzzy model approach is employed to approximate the nonlinear networked system, and a more flexible fuzzy design method is presented to get the fault detection filter which can detect the sensor fault in the presence of external disturbance. And sufficient conditions are constructed to guarantee the stability of the filter error system in terms of linear matrix inequalities. Finally, the simulation example is given to verify the feasibility of the method. [ABSTRACT FROM AUTHOR]

This paper is concerned with the dynamic event-triggered H ∞ filtering for networked systems with stochastic cyber attacks and quantization. First, a new dynamic event-triggered scheme is employed to save limited system resources by using a new triggering error, which includes the conventional one. Second, the filtering error system is formulated under the dynamic event-triggered scheme, quantization and stochastic cyber attacks in a unified framework, and independent Bernoulli distributed variables are used to describe the randomly occurring cyber attacks. Third, a new sufficient condition is established to guarantee the filtering error system is mean-square asymptotically stable and achieves a prescribed H ∞ performance. Under the condition, a new filter design method is given by applying a reversible linear transformation. Finally, a mechanical system with two masses and two springs is given to demonstrate the proposed method is more effective. [ABSTRACT FROM AUTHOR]

In this paper, the authors study the cooperative target-fencing problem for n-dimensional systems and a target with a general trajectory. Without using the velocity of the vehicles, a position feedback control law is proposed to fence the general target into the convex hull formed by the vehicles. Specifically, the dynamics of each vehicle is described by a double-integrator system. Two potential functions are designed to guarantee connectivity preservation of the communication network and collision avoidance among the vehicles. The proposed approach can deal with a target whose trajectory is any twice continuously differentiable function of time. The effectiveness of the result is verified by a numerical example. [ABSTRACT FROM AUTHOR]

For political and economic reasons, renewable sources of energy have gained much importance in establishing a sustainable energy economy. By their very nature, however, their benefits depend on changeable weather conditions, and are unrelated to the generation and consumption patterns in industrial or home environments. This generation–dissipation disparity induces price fluctuations and threatens the stability of the supply system, yet can be alleviated by installing energy depots. While the classic methods of energy storage are hardly cost-effective, they may be supplemented, or replaced, by a distributed system of small-scale hydropower plants with ponds used as energy reservoirs. In this paper, following a rigorous mathematical argument, a dynamic model of a multi-cascade of hydropower plants is constructed, and a cost-optimal controller, with formally proven properties, is designed. On the one hand, it allows for an increase in the owners' revenue by as much as 30% (compared to a free-flow state); on the other hand, it reduces the load fluctuation imposed on the grid and the legacy supply system. Moreover, the risk of floods and droughts downstream resulting from inappropriate use of the plants is averted. [ABSTRACT FROM AUTHOR]

This work investigates a state estimation problem for linear time-invariant systems based on polarized measurement information from event sensors. To enable estimator design, a new notion of observability, namely,\epsilon-observability is defined with the precision parameter\epsilon which relates to the worst-case performance of inferring the initial state, based on which a criterion is developed to test the\epsilon-observability of discrete-time linear systems. Utilizing multisensor polarity data from event sensors and the implicit information hidden in event-triggering conditions at no-event instants, an iterative event-triggered state estimator is designed to evaluate a set containing all possible values of the state. The proposed estimator is built by outer approximation of intersecting ellipsoids that are predicted from previous state estimates and the ellipsoids inferred from received polarity information of event sensors as well as the event-triggering protocol; the estimated regions of the state derived from multisensor event measurements are fused together, the sizes of which are proved to be asymptotically bounded. Distributed implementation of the estimation algorithm utilizing a two-layer processor network of hierarchy architecture is discussed, and the temporal computational complexity of the algorithm implemented in centralized and distributed ways is analyzed. The efficiency of the proposed event-triggered state estimator is verified by numerical experiments. [ABSTRACT FROM AUTHOR]

Industrial facilities, as an essential part of socio-economic systems, are susceptible to disruptions caused by earthquakes. Such disruptions may result from direct structural damage to facilities or their loss of functionality due to impacts on their support facilities and infrastructure systems. Decisions to improve the seismic performance of industrial facilities should ideally be informed by risk (and resilience) analysis, taking into account their loss of functionality and the following recovery under the influence of various sources of uncertainty. Rather than targeting specific individual facilities like a hazardous chemical plant, our objective is to quantify the resilience of interacting industrial facilities (i.e., networked industrial facilities) in the face of uncertain seismic events while accounting for their functional dependencies on infrastructure systems. A specific facility, such as a hazardous chemical plant, can be a compound node in the network representation, interacting with other facilities and their supporting infrastructure components. In this context, a compound node is a complex system in its own right. To this end, this paper proposes a formulation to model the functionality of interacting industrial facilities and infrastructure using a system of coupled differential equations, representing dynamic processes on interdependent networked systems. The equations are subject to uncertain initial conditions and have uncertain coefficients, capturing the effects of uncertainties in earthquake intensity measures, structural damage, and post-disaster recovery process. The paper presents a computationally tractable approach to quantify and propagate various sources of uncertainty through the formulated equations. It also discusses the recovery of damaged industrial facilities and infrastructure components under resource and implementation constraints. The effects of changes in structural properties and networks' connectivity are incorporated into the governing equations to model networks' functionality recovery and quantify their resilience. The paper illustrates the proposed approach for the seismic resilience analysis of a hypothetical but realistic shipping company in the city of Memphis in Tennessee, United States. The example models the effects of dependent water and power infrastructure systems on the functionality disruption and recovery of networked industrial facilities subject to seismic hazards. [ABSTRACT FROM AUTHOR]

This paper investigates the consensus of impulsive coupled networked delay systems (NDSs) under edge-based attacks. First, an edge-based attack model is established, which is asynchronous and can change the states of nodes. Next, according to the number of edges allowed to be attacked at a time instant, the edge-based attack is divided into two cases. For each case, the sufficient condition is given to guarantee the system consensus against the edge-based attacks. Then, by considering a special type of edge-based attacks on the NDSs with star topology, a less conservative consensus criterion is obtained. Finally, two numerical examples are given to verify the correctness of the obtained results. [ABSTRACT FROM AUTHOR]

In this paper, the integral sliding mode (ISM, SM) controller is designed to address the problem of implementing non-periodic sampled data for a class of networked linear systems with matched and unmatched uncertainties. Due to the redesigned gain of the nominal controller, the feedback control used by the nominal controller guarantees the asymptotic stability of the uncertain networked linear system. The discontinuous control uses intermittent control based on the reaching law to achieve the finite-time reachability of practical SM band. Based on the defined measurement error, the event-triggered (ET) condition can be derived, and furthermore, it guarantees a sufficient condition for the existence of the actual SM. On this basis, a quantization scheme is added to further decrease the network transmission burden of the linear system. No Zeno behavior occurs in the system owing to the existence of a positive lower bound of inter-event time. Compared with the conventional integral sliding mode control (ISMC, SMC), the proposed control law can not only relieve the network burden, but also decrease the transmission energy loss. Finally, simulation results of a numerical example and a mass-spring damping system demonstrate the effectiveness of the proposed method.

This paper examines the distributed filtering and fixed-point smoothing problems for networked systems, considering random parameter matrices, time-correlated additive noises and random deception attacks. The proposed distributed estimation algorithms consist of two stages: the first stage creates intermediate estimators based on local and adjacent node measurements, while the second stage combines the intermediate estimators from neighboring sensors using least-squares matrix-weighted linear combinations. The major contributions and challenges lie in simultaneously considering various network-induced phenomena and providing a unified framework for systems with incomplete information. The algorithms are designed without specific structure assumptions and use a covariance-based estimation technique, which does not require knowledge of the evolution model of the signal being estimated. A numerical experiment demonstrates the applicability and effectiveness of the proposed algorithms, highlighting the impact of observation uncertainties and deception attacks on estimation accuracy.

Energy efficiency, real-time response, and data transmission reliability are important objectives during networked systems design. This paper aims to develop an efficient task mapping scheme to balance these important but conflicting objectives. To achieve this goal, tasks are triplicated to enhance reliability and mapped on the wireless nodes of the networked systems with Dynamic Voltage and Frequency Scaling (DVFS) capabilities to reduce energy consumption while still meeting real-time constraints. Our contributions include the mathematical formulation of this task mapping problem as mixed-integer programming that balances node energy consumption, enhancing data reliability, under real-time and energy constraints. Compared with the State-of-the-Art (SoA), a joint-design problem is considered in this paper, where DVFS, task triplication, task allocation, and task scheduling are optimized concurrently. To find the optimal solution, the original problem is linearized, and a decomposition-based method is proposed. The optimality of the proposed method is proved rigorously. Furthermore, a heuristic based on the greedy algorithm is designed to reduce the computation time. The proposed methods are evaluated and compared through a series of simulations. The results show that the proposed triplication-based task mapping method on average achieves 24.84% runtime reduction and 28.62% energy saving compared to the SoA methods. [ABSTRACT FROM AUTHOR]

This paper treats the problem of H ∞ fault detection for discrete-time networked systems under deception attacks, communication delays, and perturbations. To this end, a mode-dependent fault detection filter (FDF) is presented as a residual generator to detect the occurrence of the fault. As phenomena are occurring randomly via network communication, both the delays and attacks can be appropriately described by mutually independent Markov stochastic process and Bernoulli random variable, respectively. By using the Lyapunov stability theory and linear matrix inequality (LMI) technique, a new sufficient condition for the H ∞ performance analysis of the augmented systems is obtained. Furthermore, and based on the obtained condition, the mode-dependent FDF that ensures the stochastic stability and a prescribed H ∞ performance level of the corresponding augmented system is designed in terms of linear matrix inequalities (LMIs). Finally, a numerical example is presented to show usefulness of the designed mode-dependent FDF. [ABSTRACT FROM AUTHOR]

This paper considers a deterministic susceptible-infected-susceptible (SIS) networked bivirus epidemic model (termed the bivirus model for short), in which two competing viruses spread through a set of populations (nodes) connected by two graphs, which may be different if the two viruses have different transmission pathways. The networked dynamics can give rise to complex equilibria patterns, and most current results identify conditions on the model parameters for convergence to the healthy equilibrium (where both viruses are extinct) or a boundary equilibrium (where one virus is endemic and the other is extinct). However, there are only limited results on coexistence equilibria (where both viruses are endemic). This paper establishes a set of "counting" results which provide lower bounds on the number of coexistence equilibria and, perhaps more importantly, establish properties on the local stability/instability properties of these equilibria. In order to do this, we employ the Poincaré--Hopf theorem but with significant modifications to overcome several challenges arising from the bivirus system model, such as the fact that the system dynamics do not evolve on a manifold in the typical sense required to apply Poincaré--Hopf theory. Subsequently, Morse inequalities are used to tighten the counting results, under the reasonable assumption that the bivirus system is a Morse--Smale dynamical system. Numerical examples are provided which demonstrate the presence of multiple attractor equilibria and multiple coexistence equilibria. [ABSTRACT FROM AUTHOR]

In this paper, we study collective decision‐making in a multi‐population framework, where groups of individuals represent whole populations that interact by means of a regular network. Each group consists of a number of players and every player can choose between two options. A group is characterized by three variables, the first two denoting the fractions of individuals committed to each option and the last one representing the fraction of players not committed to either option. The state of every population is influenced by the state of neighboring groups. The contribution of this work is the following. First, we find explicit expressions for all the equilibrium points of the proposed system, and show that these represent equilibrium points where populations reach consensus, namely, where all populations have the same states. We also derive a sufficient condition for local asymptotic stability as well as exponential asymptotic stability. Then, we study a structured model where every population is now assumed to represent a structured complex network. We conclude the paper by providing a set of simulations that corroborate the theoretical findings. [ABSTRACT FROM AUTHOR]

Analysis of modern trends, advanced concepts, and projects in the field of information technologies shows the growing role of artificial intelligence, primarily decentralized artificial intelligence. The paper analyzes the advanced concepts of building new generation applications in the field of information technologies and relevant applied developments and provides a brief analysis of modern achievements in the field of decentralized artificial intelligence and self-organization, which are able to support the practical implementation of such applications. A simplified analog of the roadmap for the development of decentralized and self-organizing artificial intelligence for the near future has also been formulated. [ABSTRACT FROM AUTHOR]

This paper considers the robust H ∞ performance problem of continuous-time uncertain large-scale networked systems (LSNSs). The systems consist of numerous arbitrarily connected subsystems, each of which has different dynamics. Currently it is computationally difficult to manage systems with the existing lumped analysis method; therefore, exploiting the structural properties of the systems, sufficient conditions are derived for robust H ∞ performance. Based on these results, an analysis condition that depends only on the parameters of each single subsystem is further obtained. Some numerical simulations are proposed to verify the validity and superiority of the developed conditions. [ABSTRACT FROM AUTHOR]

Complex systems composed of multiple interconnected sub-systems need to be controlled with specialized control algorithms. In this paper, two classes of control algorithms suitable for such processes are presented. Firstly, two distributed model predictive control (DMPC) strategies with different formulations are described. Afterward, a coalitional control (CC) strategy is proposed, with two different communication topologies, i.e., a default decentralized topology and a distributed topology. All algorithms were tested on the same simulation setup consisting of eight water tanks. The simulation results show that the coalitional control methodology has a similar performance to the distributed algorithms. Moreover, due to its simplified formulation, the former can be easily tested on embedded systems with limited computation storage. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the finite-time boundedness problems for large-scale continuous-time networked dynamical systems constituted by lots of subsystems. The interactions among these subsystems are arbitrary, and every subsystem has different dynamics. The linear time-varying and time-invariant cases are discussed respectively. Sufficient conditions for the design of finite-time boundedness state feedback controller are derived, which efficiently utilize the characteristic of system structure with the block-diagonal structure of system parameter matrices and the sparseness of the system topology. Sufficient conditions depending only on parameter matrices of the individual subsystem are also provided. Furthermore, sufficient conditions are provided for the design of the distributed output feedback controller with finite-time boundedness. Several numerical simulations have been used to show the validity of the derived conditions in the analysis of a large-scale networked system.

This paper studies the trade-off between the degree of decentralization and the performance of a distributed controller in a linear-quadratic control setting. We study a system of interconnected agents over a graph and a distributed controller, called κ-distributed control, which lets the agents make control decisions based on the state information within distance κ on the underlying graph. This controller can tune its degree of decentralization using the parameter κ and thus allows a characterization of the relationship between decentralization and performance. We show that under mild assumptions, including stabilizability, detectability, and a subexponentially growing graph condition, the performance difference between κ-distributed control and centralized optimal control becomes exponentially small in κ. This result reveals that distributed control can achieve near-optimal performance with a moderate degree of decentralization, and thus it is an effective controller architecture for large-scale networked systems. [ABSTRACT FROM AUTHOR]

Selecting appropriate inputs for systems described by complex networks is an important but difficult problem that largely remains open in the field of control of networks. Recent work has proposed two methods for energy efficient input selection; a gradient-based heuristic and a greedy approximation algorithm. We propose here an alternative method for input selection based on the analytic solution of the controllability Gramian of the 'balloon graph', a special model graph that captures the role of both distance and redundant paths between a driver node and a target node. The method presented is especially applicable for large networks where one is interested in controlling only a small number of outputs, or target nodes, for which current methods may not be practical because they require computing a typically very ill-conditioned matrix, called the controllability Gramian. Our method produces comparable results to the previous methods while being more computational efficient. [ABSTRACT FROM AUTHOR]

In this paper, the fusion estimation problem is investigated for a class of multi-sensor networked systems with unknown-but-bounded noises and mixed time-delays under try-once-discard protocols (TODPs). The measurements of multiple sensor nodes are sent to the remote fusion center through individual network channels, where the TODP is implemented in each channel to schedule the signal transmissions for the purpose of avoiding data collisions. To fuse the information collected from all channels, a sequential estimator is proposed whose estimator parameters are designed such that the estimation error (after each measurement update) is restrained into a zonotope with minimum F -radius at each moment. First, by using the properties of zonotopes, desired zonotopes are obtained that contain the estimation errors, and the F -radii of these zonotopes are subsequently minimized by appropriately designing the estimator parameters. It is shown that, under the proposed zonotopes-based fusion rule, the designed sequential estimator leads to the same estimation accuracy as that of the widely utilized parallel estimator. Finally, an illustrative example is provided to verify the validity of the developed fusion estimation method. • The fusion estimation problem is investigated for multi-sensor networked systems. • Mixed time-delays under try-once-discard protocols are considered. • A sequential estimator is proposed by using the properties of zonotopes. • The desired zonotopes are obtained that contain the estimation errors. [ABSTRACT FROM AUTHOR]

For political and economic reasons, renewable sources of energy have gained much importance in establishing a sustainable energy economy. By their very nature, however, their benefits depend on changeable weather conditions, and are unrelated to the generation and consumption patterns in industrial or home environments. This generation–dissipation disparity induces price fluctuations and threatens the stability of the supply system, yet can be alleviated by installing energy depots. While the classic methods of energy storage are hardly cost-effective, they may be supplemented, or replaced, by a distributed system of small-scale hydropower plants with ponds used as energy reservoirs. In this paper, following a rigorous mathematical argument, a dynamic model of a multi-cascade of hydropower plants is constructed, and a cost-optimal controller, with formally proven properties, is designed. On the one hand, it allows for an increase in the owners’ revenue by as much as 30% (compared to a free-flow state); on the other hand, it reduces the load fluctuation imposed on the grid and the legacy supply system. Moreover, the risk of floods and droughts downstream resulting from inappropriate use of the plants is averted.

This paper presents two new fault models for networked systems. These fault models are more realistic and generalized for networked systems in the sense that they can represent the effects of fault at the node and network levels. At the network layer, the uncertain effects of the network lines are modeled using a Markov chain with complex transition probabilities simultaneously with the stochastic behavior of the network using a Bernoulli process. A new output feedback-based controller, which is two-mode dependent and considers network uncertainties and output measurements for gain calculation, is presented. Using the tools of robust control and stochastic stability, linear matrix inequality-based sufficient conditions are derived. The proposed controller successfully maintains the system's performance by tolerating the effects of simultaneous sensor and actuator faults, ensuring the stability of networked loops. Simulation results verify the applicability of the presented fault-tolerant control against multiple simultaneous faults. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The design of recursive estimation algorithms in networked systems is an important research challenge from both theoretical and practical perspectives. The growing number of application fields are demanding the development of new mathematical models and algorithms that accommodate the effect of the unavoidable network-induced uncertainties. Special relevance have transmission delays and packet dropouts, which may yield a significant degradation in the performance of conventional estimators. This paper discusses the distributed fusion estimation problem in a class of linear stochastic uncertain systems whose measurement noises are cross-correlated and coupled with the process noise. The uncertainty of the system is not only described by additive noises, but also by multiplicative noise in the state equation and random parameter matrices in the measurement model. Both one-step delays and packet dropouts can randomly occur during the transmission of the sensor measurements to the local processors and a compensation strategy based on measurement prediction is used. Under the least-squares criterion and using an innovation approach, a recursive algorithm for the local filtering estimators is designed. These local estimators are then fused at a processing centre, where the distributed fusion filter is generated as the least-squares matrix-weighted linear combination of the local ones. [ABSTRACT FROM AUTHOR]

We consider the average consensus problem for the multi‐agent system in the discrete‐time domain. Three triggering based control protocols are developed, which dictate the broadcast and control update instants of individual agents to alleviate communication and computational burden. Lyapunov‐based design methods prescribe when agents should communicate and update their control so that the network converges to the average of agents' initial states. We start with a static version of the distributed event‐triggering law and then generalize it so that it involves an internal auxiliary variable to regulate the threshold dynamically for each agent. The third protocol uses a self‐triggering algorithm to avoid continuous listening wherein each agent estimates its next triggering time and broadcasts it to its neighbors at the current triggering time. Numerical simulations are shown to validate the efficacy of the proposed algorithms. [ABSTRACT FROM AUTHOR]

The observer design and dynamic output feedback control for a class of nonlinear networked systems are studied in this paper. The model of the networked systems is established by using T-S fuzzy method, and the state observer of the systems is designed when the states of the systems are unknown. On this basis, the sufficient conditions for the exponential stability of the system are explored by using the linear matrix inequality (LMI) method and Lyapunov stability theory. Then, the dynamic output feedback control of the systems is designed by using the observer states, which ensures that the states of the closed-loop systems and the error systems exponentially converge to the origin at the same time. Finally, a simulation example is given to illustrate the feasibility and effectiveness of the design method. [ABSTRACT FROM AUTHOR]

This paper considers the identification problem of network structures of interconnected dynamical systems using measured output data. In particular, we propose an identification method based on the measured output data of each node in the network whose dynamic is unknown. The proposed identification method consists of three steps: we first consider the outputs of the nodes to be all the states of the dynamics of the nodes, and the unmeasurable hidden states to be dynamical inputs with unknown dynamics. In the second step, we define the dynamical inputs as new variables and identify the dynamics of the network system with measured output data using Koopman operators. Finally, we extract the network structure from the identified dynamics as the information transmitted via the network. We show that the identified coupling functions, which represent the network structures, are actually projections of the dynamical inputs onto the space spanned by some observable functions. Numerical examples illustrate the validity of the obtained results. [ABSTRACT FROM AUTHOR]

The exponentially stable analysis and observerbased dynamic output feedback control of T-S fuzzy networked systems with state delay and communication delay are studied in this paper. The mathematical model of networked systems with state delay and communication delay is established by T-S fuzzy method. Then, the fuzzy observer of the systems is designed by using the systems output. With Lyapunov stability theorem and linear matrix inequality method, the sufficient conditions for the exponential stability of networked systems and error systems are obtained. On this basis, the design strategy of dynamic output feedback control of networked systems is given. The simulation and experimental results show that the proposed control strategy is effective and feasible. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to multi-agent reinforcement learning (RL) for linear systems with convex polytopic constraints. Existing work on RL has demonstrated the use of model predictive control (MPC) as a function approximator for the policy and value functions. The current paper is the first work to extend this idea to the multi-agent setting. We propose the use of a distributed MPC scheme as a function approximator, with a structure allowing for distributed learning and deployment. We then show that Q-learning updates can be performed distributively without introducing nonstationarity, by reconstructing a centralized learning update. The effectiveness of the approach is demonstrated on a numerical example. [ABSTRACT FROM AUTHOR]

In this article, the scheduling problem over a delayed network is investigated. Different from the existing literature, we propose a hybrid stochastic model which incorporates both delay and packet loss. Based on this proposed model, an energy-efficient co-design problem is considered under power-limited scenarios. A global optimal offline solution is presented explicitly. Aided by arriving information feedback, we propose an online schedule based on an absolute threshold. The optimality of our proposed schemes and thresholds selection is theoretically analyzed. Meanwhile, we have extended the conclusions of the single-system case to the multisystem case. Optimal strategies from both offline and online perspectives are provided with rigorous proof, respectively. The stability condition is given for the estimator to converge in an infinite-time horizon. The comparisons between offline and online strategies are proved the global superiority of the online strategy to the offline one. Numerical simulations have validated the correctness and superiority of our proposed algorithms. [ABSTRACT FROM AUTHOR]

Highlighted by success stories like AlphaGo, reinforcement learning (RL) has emerged as a powerful tool for decision making in complex environments. However, the success of RL has thus far been limited to small-scale or single-agent systems. To apply RL to large-scale networked systems such as energy, transportation, and communication networks, a critical hurdle is the curse of dimensionality, because for these systems, the state and action space can be exponentially large in the number of nodes in the network. This article attempts to break this curse of dimensionality and designs a scalable RL method, named scalable actor critic (SAC), for large networked systems. The key technical contribution is to exploit the network structure to derive an exponential decay property, which enables the design of the SAC approach. We study reinforcement learning (RL) in a setting with a network of agents whose states and actions interact in a local manner where the objective is to find localized policies such that the (discounted) global reward is maximized. A fundamental challenge in this setting is that the state-action space size scales exponentially in the number of agents, rendering the problem intractable for large networks. In this paper, we propose a scalable actor critic (SAC) framework that exploits the network structure and finds a localized policy that is an O (ρ κ + 1) -approximation of a stationary point of the objective for some ρ ∈ (0 , 1) , with complexity that scales with the local state-action space size of the largest κ -hop neighborhood of the network. We illustrate our model and approach using examples from wireless communication, epidemics, and traffic. Funding: This work was supported by the Caltech Center for Autonomous Systems and Technologies; Office of Naval Research [Grant YIP N00014-19-1-2217]; Air Force Office of Scientific Research [Grant YIP FA9550-18-1-0150]; National Science Foundation [Grants AitF-1637598, CAREER 1553407, and CNS-1518941]; PIMCO [PIMCO Fellowship]; Amazon Web Services [Amazon AI4Science Fellowship]; and Resnick Sustainability Institute for Science, Energy and Sustainability, California Institute of Technology [Postdoctoral Fellowship]. [ABSTRACT FROM AUTHOR]

This paper investigated the fault detection problem in finite-frequency domain for nonlinear networked systems under stochastic cyber-attacks. To save limited network resource, this paper introduced a novel dynamic event-triggered scheme. Firstly, under the consideration of fault sensitivity and disturbance robustness, the paper converted the addressed fault detection problem into an auxiliary H-/Ｈ∞ filtering problem by augmenting the states of the original system and the fault detection filter. Taking sector bounded nonlinearity and stochastic cyber-attacks into consideration, the design of Hperformance index included the frequency characteristics of fault signals. Combined with finite-frequency input characteristics, the paper proposed the joint design algorithm for fault detection filter and dynamic event-triggered scheme under the finite-frequency fault input. Finally, a simulation example of stirred tank reactor system verified the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

State estimation is a fundamental task in power systems. Although distribution systems are increasingly equipped with sensing devices and smart meters, measurements are typically reported at different rates and asynchronously; these aspects pose severe strains on workhorse state estimation algorithms, which are designed to process batches of data collected in a synchronous manner from all the measurement units. In this paper, we develop a novel state estimation algorithm to continuously update the estimate of the state based on measurements received in an asynchronous manner from measurement units. The synthesis of the algorithm hinges on a proximal-point type method, implemented in an online fashion, and capable of processing measurements received sequentially from sensors. A performance analysis is presented by providing bounds on the estimation error in terms of the mean and variance that hold at each iteration and asymptotically. The scheme is also compared with a more traditional Weighted Least Squares estimator that compensates for the lack of measurement data by using, as pseudo measurements, the measurement retrieved during a certain time window. Numerical simulations on the IEEE 37-bus feeder corroborate the analytical findings. [ABSTRACT FROM AUTHOR]

In this article, the non-negative edge consensus problem is addressed for positive networked systems with undirected graphs using state-feedback protocols. In contrast to existing results, the major contributions of this work included: 1) significantly improved criteria of consequentiality and non-negativity, therefore leading to a linear programming approach and 2) necessary and sufficient criteria giving rise to a semidefinite programming approach. Specifically, an improved upper bound is given for the maximum eigenvalue of the Laplacian matrix and the (out-) in-degree of the degree matrix, and an improved consensuability and non-negativevity condition is obtained. The sufficient condition presented only requires the number of edges of a nodal network without the connection topology. Also, with the introduction of slack matrix variables, two equivalent conditions of consensuability and non-negativevity are obtained. In the conditions, the system matrices, controller gain, as well as Lyapunov matrices are separated, which is helpful for parameterization. Based on the results, a semidefinite programming algorithm for the controller is readily developed. Finally, a comprehensive analytical and numerical comparison of three illustrative examples is conducted to show that the proposed results are less conservative than the existing work. [ABSTRACT FROM AUTHOR]

For networked control systems, it is known that various communication parameters in the channel will pose some fundamental limitations on output tracking control (OTC) performance. In this study, we mainly discuss the limitations resulting from model uncertainties, involving channel and plant. Through using the bivariate stochastic process to model packet loss, and the assumption that channel noise is additive white Gaussian noise (AWGN), two explicit expressions of output tracking performance limitations are derived with the single-degree-of-freedom (SDOF) and two-degree-of-freedom (TDOF) control structure, which shows that the performance of OTC is closely related to the inherent characteristics of the plant, as well as the packet loss rate and power spectral density (PSD) of AWGN. Finally, by considering an illustrative example, the simulation results are verified and analyzed to ensure the effectiveness of treatment methods and results. [ABSTRACT FROM AUTHOR]

This paper considers the robust H∞ performance problem of continuous-time uncertain large-scale networked systems (LSNSs). The systems consist of numerous arbitrarily connected subsystems, each of which has different dynamics. Currently it is computationally difficult to manage systems with the existing lumped analysis method; therefore, exploiting the structural properties of the systems, sufficient conditions are derived for robust H∞ performance. Based on these results, an analysis condition that depends only on the parameters of each single subsystem is further obtained. Some numerical simulations are proposed to verify the validity and superiority of the developed conditions.