Distributed Control Mechanism Research Articles

Adaptive fuzzy event-triggered control for a class of nonlinear time-delay multi-agent systems with dead zone and partial state constraints

This paper presents an adaptive fuzzy event-triggered control (ETC) method for a class of time-delay nonlinear multi-agent systems (MASs) with dead zone (DZ) and partial state constraints (PSCs). It should be pointed out that the states considered in this paper are unmeasurable and part of the system states are constrained by time-varying boundary. By utilizing fuzzy logic systems (FLSs) and backstepping design framework, a fuzzy state observer is constructed to estimate the unmeasured states, while the introduction of a time-varying barrier Lyapunov function (BLF) addresses the partial state constrained problem. Furthermore, during the design process of the state observer, compensation for the influence of the unknown DZ input on the system is achieved through utilization of known designed event-triggered input signal and adaptive parameter. Building upon this foundation, employing the Lyapunov stability theory and adaptive backstepping technique with ETC strategy, the developed distributed control mechanism demonstrates that all variables of the closed-loop system are bounded, the consensus error of the system can converge to a small region of origin and Zeno behavior can be ruled out. Finally, two simulation examples further illustrate the proposed control strategy and theorem.

A novel distributed finite-time economic dispatch for DC microgrids with time delays

This paper delineates an advanced distributed control paradigm for finite-time generation cost optimization in DC Microgrids (MGs), taking into account time delays. Firstly, to enhance system stability with time delays, Artstein's reduction approch is utilized to convert the time-delayed system into an equivalent delay-free model. Subsequently, a finite-time consensus-based distributed algorithm for cost optimization is formulated to equilibrate the incremental costs (ICs) of all distributed generators (DGs) in a predefined convergence time, while adhering to the DGs' capacity limits. Moreover, a distributed control mechanism for finite-time voltage stabilization is developed for sustaining the balance between the generated powers and consumptions across the MG. Rigorous convergence analysis proves that the proposed controller maintains finite-time stability. The efficacy of this innovative control strategy is substantiated through a series of comprehensive case studies.Top of Form

Distributed Adaptive Synchronization in Euler–Lagrange Networks With Uncertain Interconnections

In this work we propose a new practical synchronization protocol for multiple Euler Lagrange (EL) systems without structural linear-in-the-parameters (LIP) knowledge of the uncertainty and where the agents can be interconnected before control design by unknown state-dependent interconnection terms. This setting is meant to overcome two standard a priori assumptions in the literature concerning uncertainty with LIP structure and absence of interaction among agents before designing the synchronization protocol. To overcome these assumptions, we propose an adaptive distributed control mechanism having the purpose of estimating the coefficients of the resulting state-dependent uncertainty structure.

Fully Distributed, Event-Triggered Containment Control of Multi-Agent Systems Based on Wireless Sensor Networks and Time Base Generators

In this study, wireless sensor networks and time base generators are used to solve the fixed-time containment control problem in multi-agent systems with fixed topologies. A new event-triggered control protocol is proposed, which combines a fully distributed method and a time base generator (TBG). The goal is to converge the states of all followers to the convex hull formed by the leader. The controller reduces communication and improves control efficiency by integrating a fully distributed control mechanism using wireless sensor networks. In addition, a time base generator (TBG) is added to ensure that the dwell time continues to be pre-specified and independent of initial conditions. Using matrix theory, the original system is transformed into an error system, and its stability is analyzed by the Lyapunov method. The necessary and sufficient conditions for solving the time consensus containment control problem in multi-agent systems are determined and Zeno behavior is avoided. The effectiveness of the proposed control algorithm is illustrated by numerical examples.

Distributed Optimization Control for Heterogeneous Multiagent Systems under Directed Topologies

This paper focuses on the solutions for the distributed optimization coordination problem (DOCP) for heterogeneous multiagent systems under directed topologies. To begin with, a different convex optimization problem is proposed, which implies a weighted average of the objective function of each agent. Sufficient conditions are set to ensure the unique solution for the DOCP. Then, despite the external disruption, a distributed control mechanism is constructed to drive the state of each agent to the auxiliary state in a finite time. Furthermore, it is demonstrated that the outputs of all agents can achieve the optimal value, ensuring global convergence. Moreover, the controller design rule is expanded with event-triggered communication, and there is no Zeno behavior. Finally, to exemplify the usefulness of the theoretical conclusions, a simulation example is offered.

Resilient finite-time distributed event-triggered consensus of multi-agent systems with multiple cyber-attacks

This study addresses the resilient finite-time distributed event-triggered (ET) consensus control of multi-agent systems (MASs) with multiple cyber-attacks, such as replay, deception, and denial-of-service (DoS) attacks. For the first attempt, a model is proposed for multiple cyber-attacks on MASs under DoS, deception, and replay attacks, that occur stochastically in a unified framework. Furthermore, an appropriate distributed ET consensus control mechanism is proposed to handle the unwanted traffic in the network communication channel. This scheme determines whether or not the signal can be transmitted to each agent in MASs. Sufficient conditions for the consensus of MASs are derived using Lyapunov stability theory and linear matrix inequalities (LMIs), which ensure the exponentially mean-square finite-time stability. On the basis of the obtained conditions, the explicit expressions of the consensus control gain matrix and trigger parameter matrices are given. Eventually, simulation results are provided to verify the proposed theoretical results.

Transactive energy management for microgrids considering techno‐economic perspectives of utility – a review

In a power system, distributed energy resources, including renewables, are advantageous for reducing power transmission losses and environmental pollution. However, increasing penetration of distributed renewable energy sources is challenging as they may pose security issues to the grid and centralized control of quantum number of such sources is quite impossible. Microgrid based distribution system is facilitating integration of distributed renewable energy sources as they can supply the local loads and reduce burden on the grid. Moreover, distributed control mechanism of different microgrids enhances the functioning efficiency of the grid. However, intermittent and uncertain nature of renewables leads into demand-supply imbalance in a microgrid. Networked microgrids is new concept where the energy sharing between the microgrids may encourage renewable integration and reduce demand-supply imbalance. In order to manage such energy sharing among the different components in a microgrid or different microgrids in a networked structure, an appropriate energy management approach is necessitated. Transactive energy is an emerging technology for energy management in the distribution system, which uses market mechanisms for energy management. This paper discusses various types of market structures and market clearing approaches in transactive energy framework for energy management. Moreover, techno-economic perspectives of utility in transactive energy trading, such as network constraints and network usage charges, are elaborated. Finally, this review details the challenges and future scope in the transactive energy management of microgrids and networked microgrids.

Blockchain Technology in the Chemical Industry.

This article presents a review of the application of blockchain and blockchain-based smart contracts in the chemical and related industries. We introduce the basic concepts of blockchain and smart contracts and explain how some of their features are enabled. We review several typical or novel applications of blockchain and smart contract technologies and their enabling concepts and underlying technologies. We classify the selected literature into five categories and discuss their motivations and technical designs. We recognize that the trend of decentralization creates a need to use blockchain and smart contracts to implement trust and distributed control mechanisms. We also speculate on future applications of blockchain and smart contracts. We believe that, in the future, blockchains with different consensus mechanisms will be studied and applied to achieve more efficient and practical decentralized systems. Also, blockchain-based smart contracts will be more widely applied to enhance autonomous distributed controls in decentralized systems.

Deep multi‐agent Reinforcement Learning for cost‐efficient distributed load frequency control

The rise of microgrid-based architectures is modifying significantly the energy control landscape in distribution systems, making distributed control mechanisms necessary to ensure reliable power system operations. In this article, the use of Reinforcement Learning techniques is proposed to implement load frequency control (LFC) without requiring a central authority. To this end, a detailed model of power system dynamic behaviour is formulated by representing individual generator dynamics, generator rate and network constraints, renewable-based generation, and realistic load realisations. The LFC problem is recast as a Markov Decision Process, and the Multi-Agent Deep Deterministic Policy Gradient algorithm is used to approximate the optimal solution of all LFC layers, that is, primary, secondary and tertiary. The proposed LFC framework operates through centralised learning and distributed implementation. In particular, there is no information interchange between generating units during operation. Thus, no communication infrastructure is necessary and information privacy between them is respected. The proposed framework is validated through numerical results and it is shown that it can be used to implement LFC in a distributed and cost-efficient manner.

Stability Oriented Design of Cyber Attack Resilient Controllers for Cooperative DC Microgrids

Due to the importance of reliability and security in dc microgrids, it is essential to provide maximum resilience against cyberattacks. However, insufficient global information in the microgrid makes it difficult to accurately identify the location of these attacks. To address these issues, this article develops a novel resilient distributed control mechanism, which ensures average voltage regulation and proportional load sharing in dc microgrids under unknown cyberattacks. The proposed resilient control design does not require any information regarding the nature or location of the attacks. By virtue of a graph theoretical approach and a Lyapunov-based framework, the proposed resilient distributed control strategy is designed in a way such that the system stability is always guaranteed following a comprehensive design mechanism. Finally, the robustness of the proposed resilient distributed control approach is demonstrated via simulations and validated by experimental results.

Synthesizing General-Purpose Code Into Dynamically Scheduled Circuits

High-level synthesis (HLS) tools generate hardware designs from high-level programming languages and should liberate designers from the details of hardware description languages like VHDL and Verilog. HLS tools typically build datapaths that are controlled using a centralized controller, which relies on a compile-time schedule to determine the clock cycle when each operation executes. Such an approach results in high-throughput pipelined designs only in cases where memory accesses are provably independent and critical control decisions are determinable during code compilation. Unfortunately, when this is not the case, current tools must make pessimistic assumptions, yielding inferior schedules and lower performance. Recent advances in HLS have explored methods to overcome the conservatism in static scheduling and to remove the inability of HLS tools to handle dynamic events. Dataflow circuits play a significant role in this context: they are built out of units that communicate using point-to-point pairs of handshake control signals and this distributed control mechanism effectively implements a dynamic schedule, adapted at runtime to particular memory and control outcomes. Dataflow circuits can exploit the same optimization opportunities as standard HLS circuits (i.e., pipelining and resource sharing), but also introduce to HLS features similar to those of modern superscalar processors (i.e., out-of-order memory accesses and speculative execution), which are key for HLS to be successful in new contexts and broader application domains.

A Passivity-Based Framework for Stability Analysis and Control Including Power Network Dynamics

The ongoing efforts by many countries worldwide to increase the share of renewable energy sources in power generation has changed the nature of existing power grids and introduced numerous stability-related issues. To address these problems, more extensive and detailed stability analysis studies are therefore needed. Driven by this need, in this article, we present a passivity-based framework for stability analysis and control design that allows more accurate modeling of both the network and the power system components while facilitating the derivation of completely decentralized stability results. In particular, the proposed approach relies on the formulation of the network as a dynamical multivariable system, which is shown to be passive, even when the network’s dynamic and lossy nature are taken into account. The application of decentralized passivity conditions on bus dynamics is then further exploited, together with the incorporation of more accurate dynamical models for the power system components, to guarantee the asymptotic stability of the interconnected system. Moreover, we discuss the opportunities provided by the proposed approach regarding the incorporation of higher order dynamics, the derivation of significant stability results in a completely decentralized manner and the design of effective distributed control mechanisms. These opportunities are finally verified through the design of a demand-side voltage droop controller and several dynamic simulations on two typical test systems.

Two Extensions of Trust Management Languages

This article is focused on the family of role-based trust management languages (RT). Trust management languages are a useful method of representing security credentials and policies in large distributed access control mechanisms. They provide sets of credentials that are assigned to individual roles performed by the specific entities. These credentials provide relevant information about security policies issued by trusted authorities and define user permissions. RT languages describe the individual entities and the roles that these entities play in a given environment. A set of credentials representing a given security policy defines which entity has the necessary rights to access a specific resource and which entity does not have such rights. This study presents the results of research focusing on the potential of the family of RT languages. Its purpose is to show how security policies may be applied more widely by applying an inference system, and then using the extensions of the credentials, by taking into account time-related information or the conditions imposed with regard to the validity of such credentials. Each of these extensions can be used jointly or separately, offering even a wider range of opportunities

PC-RPL

We present PC-RPL , a transmission power-controlled IPv6 routing protocol for low-power and lossy wireless networks that significantly improves the end-to-end packet delivery performance under heavy traffic compared to the standard RPL. We show through actual design, implementation, and experiments that a multihop wireless network can achieve better throughput and routing stability when transmission power and routing topology are “jointly and adaptively” controlled. Our experiments show that the predominant “fixed and uniform” transmission power strategy with “link quality and hop distance”–based routing topology construction (i.e., RPL) loses significant bandwidth due to hidden terminal and load imbalance problems. We design an adaptive and distributed control mechanism for transmission power and routing topology, named PC-RPL , on top of the standard RPL routing protocol for hidden terminal mitigation and load balancing. We implement PC-RPL on real embedded devices and evaluate its performance on a 49-node multihop testbed. PC-RPL reduces total end-to-end packet losses by approximately sevenfold without increasing hop distance compared to RPL with the highest transmission power, resulting in 17% improvement in aggregate bandwidth and 64% improvement for the worst-case node by successfully alleviating both hidden terminal and load imbalance problems.

Decoupling-Based Channel Access Mechanism for Improving Throughput and Fairness in Dense Multi-Rate WLANs

Legacy IEEE 802.11 Medium Access Control (MAC) adopts the Distributed Coordination Function (DCF) mechanism, which provides the same access opportunity for all contenders. However, in dense multi-rate Wireless Local Area Networks (WLANs), the pure distributed control mechanism will cause high collision rate and performance anomaly, which results in low network utilization and wasting valuable channel resources. In this paper, we present a decoupling MAC mechanism (DMAC) based on the idea of contention/reservation to reduce collision and realize collision free data transmission. In proposed mechanism, the channel access time is partitioned into channel contention process and data transmission process. The proposed algorithm makes full use of the distributed random channel access mechanism and performs a centralized collision-free data transmission. Wherein, we also design an adaptive algorithm to adjust the length of the contention period to improve the channel utilization. Furthermore, we further propose two airtime fairness algorithms Improve-DMAC1 (I-DMAC1) and Improve-DMAC2 (I-DMAC2) for delay sensitive network and high throughput network scenarios, respectively, to solve the performance anomaly in multi-rate WLANs, based on DMAC. We verify the effectiveness of these decoupling algorithms through extensive simulations. Moreover, the simulation results show that the proposed algorithms achieve better performance than the 802.11 standard and other protocols.

Differential Game for Distributed Power Control in Device-to-Device Communications Underlaying Cellular Networks

In the formulating of power control for wireless networks, the radio channel is commonly formulated using static models of optimization or game theory. In these models, the optimization programming or static game is played from time point to time point. Therefore, this approach neglects the dynamics of the time-varying channels and assumes the statistical independence between the successive time points. In this paper, we utilize differential equations to model the wireless links, then formulate a differential game for the power control problem in device-to-device (D2D) communications underlaying cellular networks. The game players are the D2D pairs, which manage their transmit power by solving the continuous-time optimal control problems. The time-dependant cost function allows us to optimize the long-term expected cost, instead of point-wise instantaneous cost. We formulate the problem in an affine quadratic form that admits analytical solutions. The unique feedback Nash equilibrium of the game is shown to exist. From a stochastic optimal control algorithm, we design a distributed power control mechanism that converges to the game’s equilibrium. The simulation results show that the proposed approach achieves significant performance improvement compared to the point-wise based approaches.

Consensus-Based Droop Control of Isolated Micro-Grids by ADMM Implementations

Due to low inertia, high penetration of renewable generators in isolated micro-grids may result in poor frequency and voltage response under large disturbances. In order to overcome this difficulty, the distributed average consensus algorithm has been studied recently by extending local droop control mechanisms of power converters into distributed droop control. However, its convergence rate and the resilience to noises are still not satisfactory. In this paper, two implementations of consensus-based distributed droop control by the alternating direction multipliers method are proposed. It can be shown that the closed-loop system is described by the second-order vector differential equation, and the stability of every trajectory can be analyzed by the energy function approach under certain mild conditions. Real-time digital simulations are performed to validate the effectiveness of the proposed distributed droop control mechanism.

A containment based distributed finite-time controller for bounded voltage regulation & proportionate current sharing in DC microgrids

As link failure in a centralized network results into unstable behavior, the distributed control mechanism is often employed since the reliability and stability is enhanced by communicating with the neighbors thereby reducing the infrastructural cost required for communication. However, such compromised spanning loosely connected networks may sometimes lead to slow convergence or may even go unstable. Under such circumstances, it is essential to incorporate fast convergence speed using limited information for each controller to enhance operational reliability. Hence, this paper proposes a distributed secondary controller for each unit comprising of PV & batteries to achieve average regulation within predefined bounds and proportionate current sharing between units in finite time for DC microgrids for uniform energy management in each unit. To conform to the containment control philosophy, the followers converge to the leaders’ command in finite-time. To alleviate the dynamic performance in a significantly resistive network, a reverse droop methodology is adopted concurrently to the proposed distributed secondary controller thereby eliminating the issue of cascaded control loops. A Lyapunov based analysis is carried out to analyze its stability for varying control parameters alongwith a bode plot analysis to determine its stability margins. In addition to this, a time-delay analysis is carried out to calculate the maximum transmission delay that the controllers can withstand to maintain stability. To test the robustness of the designed controller, it is simulated for disturbances such as load change, communication delay, converter failure, link failure between leader-follower & two followers. Moreover, the real-time simulation using software-in-loop of the modeled system is also done to test the efficacy of the proposed strategy.

Cheat-Proof Distributed Power Control in Full-Duplex Small Cell Networks: A Repeated Game With Imperfect Public Monitoring

We address the problem of distributed power control in a two-tier cellular network, where full-duplex small cells underlay a macro cell in a co-channel deployment scenario. We first formulate the distributed power control problem as a non-cooperative game and then extend it to a repeated game with imperfect public monitoring. The repeated game formulation prevents deceitful small cells from deviating from the social optimal solution for their own benefit. We establish the existence and uniqueness of the Nash equilibrium in the formulated non-cooperative game. We also characterize the set of public perfect equilibrium for the repeated game. A two-phase distributed algorithm is proposed to achieve and enforce a Pareto optimal transmit power profile. The solution obtained by this algorithm is also social optimal. Phase 1 of the algorithm is a fully distributed learning phase based on perturbed Markov chains, where each base station individually learns a Pareto optimal operating point. Phase 2 is composed of two rules: 1) a detection rule based on Page-Hinckley test to detect cheating and 2) a punishment rule to motivate cheating base stations to cooperate. Through theoretical analysis, we prove that the proposed distributed power control mechanism achieves a public perfect equilibrium point of the formulated repeated game. The power control algorithm is also cheat-proof and needs only a small amount of information exchange among network nodes. The effectiveness of the algorithm is shown through numerical analysis. Our proposed model, algorithm, and analysis are also valid for a half-duplex system as a special case.

A novel tangent-ring TWDM metro-access optical network featuring reconfiguration and reliability

In this paper, we propose a novel tangent-ring TWDM metro-access network providing flexible reconfiguration and high reliability. With the innovate design of flexible distribution node (FDN) and distributed control mechanism (DCM), three kinds of reconfigurable TWDM-PONs can be built to achieve efficient intra and inter ONU direct communication by reusing optical distribution network (ODN). In addition, three-level protections covering both feeder and distribution fibers are also separately supported by the use of optical switches and natural advantage of single-fiber tangent ring topology. Moreover, with the use of simplified RN* and ONU*, the proposed network can upgrade smoothly and achieve pay-as-you grow. The network capacity, OSNR, cost and transmission performances are also studied, which suggests that this network has a good performance.