Lossy Communication Networks Research Articles

Multiagent Distributed Secondary Control for Energy Storage Systems With Lossy Communication Networks in DC Microgrid

For the energy storage system (ESS) with lossy communication networks of packet loss in DC microgrid, the multiagent distributed secondary control strategy is proposed to manage the ESS. The multiagent dynamic-tracking consensus protocol (DCP) based on the random packet loss model is constructed to estimate the global information for the dynamic network with lossy communication. Then the adaptive current sharing algorithm (ACSA) is designed to balance the state of charge (SoC) for ESSs with different capacities. Furthermore, the data-driven model-free adaptive control (MFAC) scheme is proposed for the single-input multiple-output (SIMO) nonlinear ESS to provide accurate voltage tracking and current sharing. Finally, the real-time simulation tests of different case studies are carried out through the OPAL-RT platform to verify the effectiveness of the designed control strategy. Simulation results can prove that the favorable capabilities of current sharing and voltage regulation as well as robustness to communication packet loss and communication faults can be achieved.

Optimal networked state estimation in the presence of multi-channel correlated packet losses

This paper studies the optimal estimation problem for linear time-invariant systems over lossy communication networks subject to multi-channel packet losses. In its full generality, we assume that the random packet losses are spatially correlated across different channels. The problem is addressed by employing a modified Kalman filter to estimate the system’s states, and the primary issue under study is the convergence of the state estimate under a mean-square criterion. This convergence is shown to be equivalent to the existence of a stabilizing solution to a modified algebraic Riccati equation. We provide a necessary and sufficient condition to characterize the existence of the stabilizing solution, and show that the solution can be computed by solving a set of linear matrix inequalities. Additionally, from a robust convergence perspective, we consider further packet losses whose probabilities are unknown and uncertain. We develop a matrix measure referred to as the critical matrix to characterize the maximal tolerance for the modified Kalman filter to converge. Specifically, we show that the mean-square estimation error is bounded if and only if the critical matrix satisfies a positive semi-definiteness condition.

Distributed consensus-based multi-agent temporal-difference learning

In this paper we propose two new distributed consensus-based algorithms for temporal-difference learning in multi-agent Markov decision processes. The algorithms are of off-policy type and are aimed at linear approximation of the value function. Restricting agents’ observations to local data and communications to their small neighborhoods, the algorithms consist of: (a) local updates of the parameter estimates based on either the standard TD(λ) or the emphatic ETD(λ) algorithm, and (b) dynamic consensus scheme implemented over a time-varying lossy communication network. The algorithms are completely decentralized, allowing efficient parallelization and applications where the agents may have different behavior policies and different initial state distributions while evaluating a common target policy. It is proved under nonrestrictive assumptions that the proposed algorithms weakly converge to the solutions of the mean ordinary differential equation (ODE) common for all the agents. It is also proved that the whole system may be stabilized by a proper choice of the network and that the parameter estimates weakly converge to consensus. Discussion is given on the asymptotic bias and variance of the estimates, on the projected forms of the proposed algorithms, as well as on restrictiveness of the adopted assumptions. Simulation results illustrate the main properties of the algorithms and provide comparisons with similar schemes.

State Estimation Fusion for Linear Microgrids over an Unreliable Network

Microgrids should be continuously monitored in order to maintain suitable voltages over time. Microgrids are mainly monitored remotely, and their measurement data transmitted through lossy communication networks are vulnerable to cyberattacks and packet loss. The current study leverages the idea of data fusion to address this problem. Hence, this paper investigates the effects of estimation fusion using various machine-learning (ML) regression methods as data fusion methods by aggregating the distributed Kalman filter (KF)-based state estimates of a linear smart microgrid in order to achieve more accurate and reliable state estimates. This unreliability in measurements is because they are received through a lossy communication network that incorporates packet loss and cyberattacks. In addition to ML regression methods, multi-layer perceptron (MLP) and dependent ordered weighted averaging (DOWA) operators are also employed for further comparisons. The results of simulation on the IEEE 4-bus model validate the effectiveness of the employed ML regression methods through the RMSE, MAE and R-squared indices under the condition of missing and manipulated measurements. In general, the results obtained by the Random Forest regression method were more accurate than those of other methods.

Resilient decentralized optimization of chance constrained electricity-gas systems over lossy communication networks

With the gradual growth of natural gas units, the coupling between power networks and natural gas networks has deepened, and the synergistic operation between them has become more and more important. At the same time, the integration of uncertain renewable energy brings challenges to the economic and safe operation of power and natural gas interconnected systems. In order to cope with the operational risks brought by uncertain wind sources to the interconnected system, this paper adopts the method of distributionally robust chance constraints, where an ambiguity set related to the moment information of a small amount of historical wind power forecast error data is attained in a data-driven way. Then the chance constrained problem is transformed into a formation that is easily solved. For different situations with or without a central coordinator of the two entities of electricity and gas, this work respectively gives the solution steps based on the relaxed alternating direction method of multipliers. In the case of no coordinator, this paper particularly presents the convergence performance when the exchange of information has a packet loss rate during iterative calculation processes. The simulation results show that compared with the method of traditional Gaussian chance constrained optimization and symmetrical distributionally robust chance constrained optimization, the moment-based distributionally robust chance constrained optimization can achieve a lowest probability of chance constraint violation at the expense of energy dispatch costs. In addition, compared with the traditional alternating direction method of multipliers, the relaxed alternating direction method of multipliers can achieve the nearly same energy dispatch costs with a smaller number of iterations. It is also found that the packet loss of the early iteration process may be the main factor that has a degradation impact on the convergence process rather than the nominal loss probability.

Estimating and Controlling the Renewable Microgrid States Using IoT Infrastructure

AbstractThis paper considers the optimal filter design issue for microgrid state estimation and stabilisation under packet loss conditions. The considered internet of things (IoT) technologies collect system state information from sensors and send control messages to actuators where they are connected over a lossy communication network. From signal processing point of view, this paper develops an optimal state estimation algorithm using the mean squared error principle. Then a feedback controller is designed under the condition of packet dropouts in the communication network. Finally, the convergence of the developed algorithm is numerically shown. The performance of the proposed schemes are demonstrated using the renewable microgrid.

An Optimal LQG Controller for Stochastic Event-triggered Scheduling over a Lossy Communication Network

Abstract We consider a networked control loop in which the sensors acquire partial state information and communicate to a remote controller through a lossy communication network. A scheduler, collocated with the sensors, decides to transmit a locally estimated state to the controller based on an event-triggered transmission policy with stochastic thresholds. Assuming that the local estimator either senses the communication channel or receives an ideal acknowledgment from the remote estimator, then the optimal control law can be shown to be a linear function of the conditional expectation of the state. However, the probability distribution of the state conditioned on the information available to the controller based on the mentioned transmission policy and network is not Gaussian, but rather described by a sum of Gaussians with an increasing number of terms at every time-step. We show that the optimal LQG control law can be determined without tracking this probability distribution for finding its expected value. Moreover, we establish that the stochastic event-triggered scheduler can be appropriately regulated in order to achieve a desired triggering probability at every time-step.

Sampled-Data State Observation over Lossy Networks under Round-Robin Scheduling

In this paper, we study the problem of continuous-time state observation over lossy communication networks. We consider the situation in which the samplers for measuring the output of the plant are spatially distributed and their communication with the observer is scheduled according to a round-robin scheduling protocol. We allow the observer gains to dynamically change in synchronization with the scheduling of communications. In this context, we propose a linear matrix inequality (LMI) framework to design the observer gains that ensure the asymptotic stability of the error dynamics in continuous time. We illustrate the effectiveness of the proposed methods by several numerical simulations.

Delay-Aware Quality Optimization in Cloud-Assisted Video Streaming System

Cloud-assisted video streaming has emerged as a new paradigm to optimize multimedia content distribution over the Internet. This article investigates the problem of streaming cloud-assisted real-time video to multiple destinations (e.g., cloud video conferencing, multi-player cloud gaming, etc.) over lossy communication networks. The user diversity and network dynamics result in the delay differences among multiple destinations. This research proposes <underline>D</underline>ifferentiated cloud-<underline>A</underline>ssisted <underline>VI</underline>deo <underline>S</underline>treaming (DAVIS) framework, which proactively leverages such delay differences in video coding and transmission optimization. First, we analytically formulate the optimization problem of joint coding and transmission to maximize received video quality. Second, we develop a quality optimization framework that integrates the video representation selection and FEC (Forward Error Correction) packet interleaving. The proposed DAVIS is able to effectively perform differentiated quality optimization for multiple destinations by taking advantage of the delay differences in cloud-assisted video streaming system. We conduct the performance evaluation through extensive experiments with the Amazon EC2 instances and Exata emulation platform. Evaluation results show that DAVIS outperforms the reference cloud-assisted streaming solutions in video quality and delay performance.

Distributed Optimal Dispatch of Distributed Energy Resources Over Lossy Communication Networks

Driven by smart grid technologies, a great effort has been made in developing distributed energy resources (DERs) in recent years for improving reliability and efficiency of distribution systems. Emerging DERs require effective and efficient control and coordination in order to harvest their potential benefits. In this paper, we consider optimal DER coordination problem, where the goal is to minimize the total generation cost while meeting total demand and satisfying individual generator output limit. This paper develops a distributed algorithm for solving the optimal DER coordination problem over lossy communication networks with packet-dropping communication links. Under the assumption that the underlying communication network is strongly connected with a positive probability and the packet drops are independent and identically distributed, we show that the proposed algorithm is able to solve the optimal DER coordination problem even in the presence of packet drops. Numerical simulation results are used to validate and illustrate the proposed algorithm.

Distributed State Estimation Over Unreliable Communication Networks With an Application to Smart Grids

In contrast to the traditional centralized power system state estimation methods, this paper investigates the interconnected optimal filtering problem for distributed dynamic state estimation considering packet losses. Specifically, the power system incorporating microgrids is modeled as a state-space linear equation where sensors are deployed to obtain measurements. Basically, the sensing information is transmitted to the energy management system through a lossy communication network where measurements are lost. This can seriously deteriorate the system monitoring performance and even lose network stability. Second, as the system states are unavailable, so the estimation is essential to know the overall operating conditions of the electricity network. Availability of the system states provides designers with an accurate picture of the power network, so a suitable control strategy can be applied to avoid massive blackouts due to losing network stability. Particularly, the proposed estimator is based on the mean squared error between the actual state and its estimate. To obtain the distributed estimation, the optimal local and neighboring gains are computed to reach a consensus estimation after exchanging their information with the neighboring estimators. Then, the convergence of the developed algorithm is theoretically proved. Afterward, a distributed controller is designed based on the semidefinite programming approach. Simulation results demonstrate the accuracy of the developed approaches under the condition of missing measurements.

A sufficient bit rate condition for mean‐square stabilisation of linear systems over multiple lossy networks

This article studies the mean-square stability of quantised linear systems which transmit both measurement and control data over multiple lossy communication networks with independent and identically distributed dropouts. By modifying previous quantisation policies, it proposes a multi-stage lifted quantisation policy for systems with multiple networks. That policy allows that these networks can parallely transmit data packets, i.e. all networks simultaneously work to transmit data packets. Due to such parallel transmission, we can obtain a less conservative sufficient stabilising bit rate condition, which is determined only by the unstable eigenvalues of the concerned system and the worst (highest) dropout rate of networks and is irrelevant to the number of concerned networks.

Stability of Agent Based Distributed Model Predictive Control Over a Lossy Network

In this paper, an agent based control formulation of a large-scale cyber-physical system is proposed. Each agent can partially observe a part of the global dynamical process and estimate the associated local states through a combination of traditional Kalman filtering algorithm and consensus. The local estimates are then used for state feedback control. The optimal feedback gain of individual agents is obtained through dynamically solving a moving horizon linear quadratic optimization problem. The agents also exchange information among neighbors to coordinate the agent-wise state-feedback controls. Finally, a control decision incurring the least cost among all agents is applied to the global system. A Lyapunov function-based stability analysis is performed to obtain a bound over the degree of agent negotiation in designing the control decision. Besides, the effect of lossy communication network in control design and henceforth in global system stability is also investigated and the corresponding bound in control consensus is obtained. The theoretical results are verified via simulation of a 10-agent and a 50-agent dynamical process within a radial topology.

Leveraging the Delay-Friendliness of TCP With FEC Coding in Real-Time Video Communication

Transmission control protocol (TCP) is pervasively employed as the transport-layer solution in popular video applications (e.g., Skype, Google+, HTTP-based adaptive video streaming, etc.) for firewall traversal and network-friendliness. To remedy the shortcomings of the data retransmission mechanism in TCP, forward error correction (FEC) coding is commonly used as the application-layer error-resilient scheme in live media streaming systems. An important measurement study reveals that TCP exhibits delay-friendliness (i.e., delay-performance bias) towards traffic flows composed of small-size packets. Motivated by leveraging the delay-friendliness of TCP to optimize the real-time streaming video quality, we propose a novel FEC coding scheme dubbed C oded L ive vide O S treaming ov E r T CP (CLOSET). To achieve the optimal video quality over the lossy communication networks, we analytically formulate the constrained optimization problem of joint FEC coding and packet interleaving to minimize the effective packet loss rate. Then, we provide an approximate analysis to derive the solution for online adaption of FEC redundancy, packet size, and interleaving level. The performance of CLOSET is evaluated through extensive semi-physical emulations in Exata involving real-time encoded H.264 video streaming. Experimental results show that CLOSET outperforms the reference FEC coding schemes in terms of video peak signal-to-noise ratio (PSNR), end-to-end delay, ratio of overdue video frames, and goodput. Therefore, we recommend CLOSET for TCP-based real-time video communication systems.

Stability conditions for multi-sensor state estimation over a lossy network

This paper studies a networked state estimation problem for a spatially large linear system with a distributed array of sensors, each of which offers partial state measurements. A lossy communication network is used to transmit the sensor measurements to a central estimator where the minimum mean square error (MMSE) state estimate is computed. Under a Markovian packet loss model, we provide necessary and sufficient conditions for the stability of the estimator for any diagonalizable system in the sense that the mean of the state estimation error covariance matrix is uniformly bounded. In particular, the stability conditions for the second-order systems with an i.i.d. packet loss model are explicitly expressed as simple inequalities in terms of the largest open-loop pole and the packet loss rate.

Bit-Express: A Loss Tolerant Network Transmission via Network Coding

Lossy communication networks may be one of the most challenging issues for Transmission Control Protocol (TCP), as random loss could be erroneously interpreted into congestion due to the original mechanism of TCP. Network coding (NC) promises significant improvement in such environment thanks to its ability to mix data across time and flows. Therefore, it has been proposed to combine with TCP called TCP-NC by MIT. In this paper, we dedicated to quantifying the R, a key parameter for redundant packets, and make it close to the loss rate as much as possible, which has not been considered in the previous research. All of these are done by the sender who is completely unconscious of the network situation. Simulation results by NS2 under both wired and wireless networks showed that our method retains all the advantages of TCP-NC, and meanwhile outperforms TCP-NC and the other TCP variants in time-varying lossy networks.

Optimal control over a lossy communication network based on linear predictive compensation

This study investigates the linear quadratic Gaussian state regulation problem over a packet-dropping network. A linear predictive compensation scheme is proposed to counteract the uncontrollable packet loss. To characterise the operating state of the system, differently from the widely used Markov jump linear systems theory or multi-mode Markov chain, in this study the actual control command executed to the plant is expressed as a stochastic sequence. Moreover, the original information set is divided into several subsets, which benefits to establish the quantitative relationship between the infinite-horizon quadratic performance index and the prediction horizon. A numerical example is provided to show that there exists an optimal prediction horizon to minimise the performance index.

Stability of Kalman Filtering with Multiple Sensors Involving Lossy Communications

We study a networked state estimation problem for a linear system with multiple sensors, each of which transmits its measurements to a central estimator via a lossy communication network for computing the minimum mean-square-error (MMSE) state estimate. Under a general Markov packet loss process, we establish necessary and sufficient conditions for the stability of the estimator for any diagonalizable system in the sense that the mean of the state estimation error covariance matrix is uniformly bounded. For the second-order systems under an i.i.d. packet loss model, the stability condition is expressed as a simple inequality in terms of open-loop poles and the packet loss rate.

Compensation-based control for lossy communication networks

In this paper, we are concerned with the stability analysis and the design of stabilising compensation-based control algorithms for networked control systems (NCSs) that exhibit packet dropouts. In order to increase the robustness against packet dropouts for such NCSs, we propose a new type of model-based dropout compensator, which depends on the local dropout history. Moreover, we provide linear matrix inequality based synthesis conditions for such compensators guaranteeing robust stability. The analysis and design framework includes both worst-case-bound and stochastic models to describe the packet-dropout behaviour in both the sensor-to-controller and the controller-to-actuator channels. Numerical examples demonstrate the significantly improved robustness with respect to packet dropouts using the proposed dropout compensator, compared to using the existing zero strategy and the hold strategy.

Distributed Control of Interconnected Systems with Lossy Communication Networks

We consider the control of a linear, time-invariant plant via a distributed output feedback. The feedback consists of a set of controllers: each determines the input it applies based on its own measurements as well as the measurements received from other controllers. In this work, we assume that the communication between the controller is unreliable: some measurements sent over the network are lost. For this setup we outline the design of linear output feedback controllers such that they minimize a quadratic performance criterion. Examples illustrate the approach and its applicability.