This paper presents a novel approach to economic dispatch in smart grids equipped with diverse energy devices.This method integrates features including photovoltaic(PV)systems,energy storage coupling,varied energy rol...This paper presents a novel approach to economic dispatch in smart grids equipped with diverse energy devices.This method integrates features including photovoltaic(PV)systems,energy storage coupling,varied energy roles,and energy supply and demand dynamics.The systemmodel is developed by considering energy devices as versatile units capable of fulfilling various functionalities and playing multiple roles simultaneously.To strike a balance between optimality and feasibility,renewable energy resources are modeled with considerations for forecasting errors,Gaussian distribution,and penalty factors.Furthermore,this study introduces a distributed event-triggered surplus algorithm designed to address the economic dispatch problem by minimizing production costs.Rooted in surplus theory and finite time projection,the algorithm effectively rectifies network imbalances caused by directed graphs and addresses local inequality constraints.The algorithm greatly reduces the communication burden through event triggering mechanism.Finally,both theoretical proofs and numerical simulations verify the convergence and event-triggered nature of the algorithm.展开更多
This paper introduces a novel fully distributed economic power dispatch(EPD)strategy for distribution networks,integrating dynamic tariffs.A two-layer model is proposed:the first layer comprises the physical power dis...This paper introduces a novel fully distributed economic power dispatch(EPD)strategy for distribution networks,integrating dynamic tariffs.A two-layer model is proposed:the first layer comprises the physical power distribution network,including photovoltaic(PV)sources,wind turbine(WT)generators,energy storage systems(ESS),flexible loads(FLs),and other inflexible loads.The upper layer consists of agents dedicated to communication,calculation,and control tasks.Unlike previous EPD strategies,this approach incorporates dynamic tariffs derived from voltage constraints to ensure compliance with nodal voltage constraints.Addi-tionally,a fast distributed optimization algorithm with an event-triggered communication protocol has been developed to address the EPD problem effectively.Through mathematical and simulation analyses,the proposed algorithm's efficiency and rapid conver-gence capability are demonstrated.展开更多
The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems(a.k.a.network systems).To this end,we start by putting forth a novel distributed event-trigge...The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems(a.k.a.network systems).To this end,we start by putting forth a novel distributed event-triggering transmission strategy based on periodic sampling,under which a model-based stability criterion for the closed-loop network system is derived,by leveraging a discrete-time looped-functional approach.Marrying the model-based criterion with a data-driven system representation recently developed in the literature,a purely data-driven stability criterion expressed in the form of linear matrix inequalities(LMIs)is established.Meanwhile,the data-driven stability criterion suggests a means for co-designing the event-triggering coefficient matrix and the feedback control gain matrix using only some offline collected state-input data.Finally,numerical results corroborate the efficacy of the proposed distributed data-driven event-triggered network system(ETS)in cutting off data transmissions and the co-design procedure.展开更多
The distributed event-triggered optimization problem for multiple nonholonomic robots has been studied to minimize the global battery energy consumption. Each robot possesses its own cost function which depends on the...The distributed event-triggered optimization problem for multiple nonholonomic robots has been studied to minimize the global battery energy consumption. Each robot possesses its own cost function which depends on the state of the hand position and represents battery energy consumption. By coordinate transformation, the dynamics of the hand positions can be formulated into two groups of first-order integrators. Then the distributed event-triggered optimization algorithm is designed such that the states of robots' hand positions exponentially converge to the optimizer of the global cost function.Meanwhile, the velocity and orientation of each robot are ensured to reach zero and a certain constant, respectively. Moreover, the inter-execution time is lower bounded and the Zeno behavior is therefore naturally avoided. Numerical simulations show the effectiveness of the proposed algorithm.展开更多
This paper investigates the consensus tracking problems of second-order multi-agent systems with a virtual leader via event-triggered control. A novel distributed event-triggered transmission scheme is proposed, which...This paper investigates the consensus tracking problems of second-order multi-agent systems with a virtual leader via event-triggered control. A novel distributed event-triggered transmission scheme is proposed, which is intermittently examined at constant sampling instants. Only partial neighbor information and local measurements are required for event detection. Then the corresponding event-triggered consensus tracking protocol is presented to guarantee second-order multi-agent systems to achieve consensus tracking. Numerical simulations are given to illustrate the effectiveness of the proposed strategy.展开更多
This paper investigates the time-varying formation problem for general linear multi-agent systems using distributed event-triggered control strategy.Different from the previous works,to achieve the desired time-varyin...This paper investigates the time-varying formation problem for general linear multi-agent systems using distributed event-triggered control strategy.Different from the previous works,to achieve the desired time-varying formation,a distributed control scheme is designed in an event-triggered way,in which for each agent the controller is triggered only at its own event times.The interaction topology among agents is assumed to be switching.The common Lyapunov function as well as Riccati inequality is applied to solve the time-varying formation problem.Moreover,the Zeno behavior of triggering time sequences can be excluded for each agent.Finally,a simulation example is presented to illustrate the effectiveness of the theoretical results.展开更多
In this paper,a distributed Event-Triggered(ET)collision avoidance coordinated control for Quadrotor Unmanned Aerial Vehicles(QUAVs)is proposed based on Virtual Tubes(VTs)with flexible boundaries in the presence of un...In this paper,a distributed Event-Triggered(ET)collision avoidance coordinated control for Quadrotor Unmanned Aerial Vehicles(QUAVs)is proposed based on Virtual Tubes(VTs)with flexible boundaries in the presence of unknown external disturbances.Firstly,VTs are constructed for each QUAV,and the QUAV is restricted into the corresponding VT by the artificial potential field,which is distributed around the boundary of the VT.Thus,the collisions between QUAVs are avoided.Besides,the boundaries of the VTs are flexible by the modification signals,which are generated by the self-regulating auxiliary systems,to make the repulsive force smaller and give more buffer space for QUAVs without collision.Then,a novel ET mechanism is designed by introducing the concept of prediction to the traditional fixed threshold ET mechanism.Furthermore,a disturbance observer is proposed to deal with the adverse effects of the unknown external disturbance.On this basis,a distributed ET collision avoidance coordinated controller is proposed.Then,the proposed controller is quantized by the hysteresis uniform quantizer and then sent to the actuator only at the ET instants.The boundedness of the closed-loop signals is verified by the Lyapunov method.Finally,simulation and experimental results are performed to demonstrate the superiority of the proposed control method.展开更多
For islanded microgrids(MGs),distributed control is regarded as a preferred alternative to centralized control for the frequency restoration of MGs.However,distributed control with successive communication restricts t...For islanded microgrids(MGs),distributed control is regarded as a preferred alternative to centralized control for the frequency restoration of MGs.However,distributed control with successive communication restricts the efficiency and resilience of the control system.To address this issue,this paper proposes a distributed event-triggered control strategy for the frequency secondary control in islanded MGs.The proposed event-triggered control is Zeno behavior free and enables each DG to update and propagate its state to neighboring DGs only when a specific“event”occurs,which significantly reduces the communication burden.Compared with the existing event-triggered control,a trigger condition checking period of the proposed event-triggered control is provided to reduce the computation burden when checking the trigger condition.Furthermore,using the aperiodicity and intermittent properties of the communication,a simple detection principle is proposed to detect and isolate the compromised communication links in a timely and economic fashion,which improves the resilience of the system against FDI attacks.Finally,the control effectiveness of the proposed control scheme is validated by the simulation results of the tests on an MG with 4 DGs.展开更多
We develop a policy of observer-based dynamic event-triggered state feedback control for distributed parameter systems over a mobile sensor-plus-actuator network.It is assumed that the mobile sensing devices that prov...We develop a policy of observer-based dynamic event-triggered state feedback control for distributed parameter systems over a mobile sensor-plus-actuator network.It is assumed that the mobile sensing devices that provide spatially averaged state measurements can be used to improve state estimation in the network.For the purpose of decreasing the update frequency of controller and unnecessary sampled data transmission, an efficient dynamic event-triggered control policy is constructed.In an event-triggered system, when an error signal exceeds a specified time-varying threshold, it indicates the occurrence of a typical event.The global asymptotic stability of the event-triggered closed-loop system and the boundedness of the minimum inter-event time can be guaranteed.Based on the linear quadratic optimal regulator, the actuator selects the optimal displacement only when an event occurs.A simulation example is finally used to verify that the effectiveness of such a control strategy can enhance the system performance.展开更多
Within the context of ground-air cooperation,the distributed formation trajectory tracking control problems for the Heterogeneous Multi-Agent Systems(HMASs)is studied.First,considering external disturbances and model ...Within the context of ground-air cooperation,the distributed formation trajectory tracking control problems for the Heterogeneous Multi-Agent Systems(HMASs)is studied.First,considering external disturbances and model uncertainties,a graph theory-based formation control protocol is designed for the HMASs consisting of Unmanned Aerial Vehicles(UAVs)and Unmanned Ground Vehicles(UGVs).Subsequently,a formation trajectory tracking control strategy employing adaptive Fractional-Order Sliding Mode Control(FOSMC)method is developed,and a Feedback Multilayer Fuzzy Neural Network(FMFNN)is introduced to estimate the lumped uncertainties.This approach empowers HMASs to adaptively follow the expected trajectory and adopt the designated formation configuration,even in the presence of various uncertainties.Additionally,an event-triggered mechanism is incorporated into the controller to reduce the update frequency of the controller and minimize the communication exchange among the agents,and the absence of Zeno behavior is rigorously demonstrated by an integral inequality analysis.Finally,to confirm the effectiveness of the proposed formation control protocol,some numerical simulations are presented.展开更多
The article investigates the optimal energy management(OEM)problem for microgrids.To figure out the problem in fixed time and alleviate communication load with limited resources,this article devises a novel fixed-time...The article investigates the optimal energy management(OEM)problem for microgrids.To figure out the problem in fixed time and alleviate communication load with limited resources,this article devises a novel fixed-time stability lemma and an event-triggered(ET)fixed-time distributed OEM approach.Using Lyapunov stability theory,the distributed approach has been proven to converge in fixed time and the upper bound on convergence time can be derived without dependence on the initial states.The dynamic ET method is raised to dynamically adjust the triggering threshold and reduce communication redundancy.In addition,Zeno behavior is avoided.Simulations are given to show the effectiveness and advantage of the designed distributed OEM method.展开更多
We study the contain men t cont rol problem for high-order het erogeneous nonlinear multi-age nt systems under distributed event-triggered schemes.To achieve the containment control objective and reduce communication ...We study the contain men t cont rol problem for high-order het erogeneous nonlinear multi-age nt systems under distributed event-triggered schemes.To achieve the containment control objective and reduce communication consumption among agents,a distributed event-triggered control scheme is proposed by applying the backstepping method,Lyapunov functional approach,and neural networks.Then,the results are extended to the self-triggered control case to avoid continuous monitoring of state errors.The developed protocols and triggered rules ensure that the output for each follower converges to the convex hull spanned by multi-leader signals within a bounded error.In addition,no agent exhibits Zeno behavior.Two numerical simulations are finally presen ted to verify the correctness of the obtained results.展开更多
After a century of relative stability in the electricity sector,the widespread adoption of distributed energy resources,along with recent advancements in computing and communication technologies,has fundamentally alte...After a century of relative stability in the electricity sector,the widespread adoption of distributed energy resources,along with recent advancements in computing and communication technologies,has fundamentally altered how energy is consumed,traded,and utilized.This change signifies a crucial shift as the power system evolves from its traditional hierarchical organization to a more decentralized approach.At the heart of this transformation are innovative energy distribution models,like peer-to-peer(P2P)sharing,which enable communities to collaboratively manage their energy resources.The effectiveness of P2P sharing not only improves the economic prospects for prosumers,who generate and consume energy,but also enhances energy resilience and sustainability.This allows communities to better leverage local resources while fostering a sense of collective responsibility and collaboration in energy management.However,there is still no extensive implementation of such sharing models in today’s electricitymarkets.Research on distributed energy P2P trading is still in the exploratory stage,and it is particularly important to comprehensively understand and analyze the existing distributed energy P2P trading market.This paper contributes with an overview of the P2P markets that starts with the network framework,market structure,technical approach for trading mechanism,and blockchain technology,moving to the outlook in this field.展开更多
The distributed permutation flow shop scheduling problem(DPFSP)has received increasing attention in recent years.The iterated greedy algorithm(IGA)serves as a powerful optimizer for addressing such a problem because o...The distributed permutation flow shop scheduling problem(DPFSP)has received increasing attention in recent years.The iterated greedy algorithm(IGA)serves as a powerful optimizer for addressing such a problem because of its straightforward,single-solution evolution framework.However,a potential draw-back of IGA is the lack of utilization of historical information,which could lead to an imbalance between exploration and exploitation,especially in large-scale DPFSPs.As a consequence,this paper develops an IGA with memory and learning mechanisms(MLIGA)to efficiently solve the DPFSP targeted at the mini-malmakespan.InMLIGA,we incorporate a memory mechanism to make a more informed selection of the initial solution at each stage of the search,by extending,reconstructing,and reinforcing the information from previous solutions.In addition,we design a twolayer cooperative reinforcement learning approach to intelligently determine the key parameters of IGA and the operations of the memory mechanism.Meanwhile,to ensure that the experience generated by each perturbation operator is fully learned and to reduce the prior parameters of MLIGA,a probability curve-based acceptance criterion is proposed by combining a cube root function with custom rules.At last,a discrete adaptive learning rate is employed to enhance the stability of the memory and learningmechanisms.Complete ablation experiments are utilized to verify the effectiveness of the memory mechanism,and the results show that this mechanism is capable of improving the performance of IGA to a large extent.Furthermore,through comparative experiments involving MLIGA and five state-of-the-art algorithms on 720 benchmarks,we have discovered that MLI-GA demonstrates significant potential for solving large-scale DPFSPs.This indicates that MLIGA is well-suited for real-world distributed flow shop scheduling.展开更多
This article explores the leader-following consensus tracking(LFCK)control issues of multi-agent systems(MASs)in the presence of external disturbances and general directed fixed communication topology.Its purpose is t...This article explores the leader-following consensus tracking(LFCK)control issues of multi-agent systems(MASs)in the presence of external disturbances and general directed fixed communication topology.Its purpose is to enable all follower agents to achieve consensus tracking for the leader agent.Firstly,this article introduces an extended state observer for estimating each follower agent's unknown state and external disturbance.Subsequently,on the basis of the above-extended state observer and a dynamic event-triggered strategy,a distributed consensus tracking control protocol with disturbances restraint is developed,which can reduce the MAS's update frequency on the premise of ensuring the control protocol's effectiveness.Furthermore,the MAS's stability and the absence of Zeno behavior are analyzed and proved by the established Lyapunov functional and linear matrix inequality theory.Finally,the validity and feasibility of the proposed approach are validated through a group of comparative numerical simulation experiments.展开更多
A centralized-distributed scheduling strategy for distribution networks based on multi-temporal and hierarchical cooperative game is proposed to address the issues of difficult operation control and energy optimizatio...A centralized-distributed scheduling strategy for distribution networks based on multi-temporal and hierarchical cooperative game is proposed to address the issues of difficult operation control and energy optimization interaction in distribution network transformer areas,as well as the problem of significant photovoltaic curtailment due to the inability to consume photovoltaic power locally.A scheduling architecture combiningmulti-temporal scales with a three-level decision-making hierarchy is established:the overall approach adopts a centralized-distributed method,analyzing the operational characteristics and interaction relationships of the distribution network center layer,cluster layer,and transformer area layer,providing a“spatial foundation”for subsequent optimization.The optimization process is divided into two stages on the temporal scale:in the first stage,based on forecasted electricity load and demand response characteristics,time-of-use electricity prices are utilized to formulate day-ahead optimization strategies;in the second stage,based on the charging and discharging characteristics of energy storage vehicles and multi-agent cooperative game relationships,rolling electricity prices and optimal interactive energy solutions are determined among clusters and transformer areas using the Nash bargaining theory.Finally,a distributed optimization algorithm using the bisection method is employed to solve the constructed model.Simulation results demonstrate that the proposed optimization strategy can facilitate photovoltaic consumption in the distribution network and enhance grid economy.展开更多
With the increasing popularity of blockchain applications, the security of data sources on the blockchain is gradually receiving attention. Providing reliable data for the blockchain safely and efficiently has become ...With the increasing popularity of blockchain applications, the security of data sources on the blockchain is gradually receiving attention. Providing reliable data for the blockchain safely and efficiently has become a research hotspot, and the security of the oracle responsible for providing reliable data has attracted much attention. The most widely used centralized oracles in blockchain, such as Provable and Town Crier, all rely on a single oracle to obtain data, which suffers from a single point of failure and limits the large-scale development of blockchain. To this end, the distributed oracle scheme is put forward, but the existing distributed oracle schemes such as Chainlink and Augur generally have low execution efficiency and high communication overhead, which leads to their poor applicability. To solve the above problems, this paper proposes a trusted distributed oracle scheme based on a share recovery threshold signature. First, a data verification method of distributed oracles is designed based on threshold signature. By aggregating the signatures of oracles, data from different data sources can be mutually verified, leading to a more efficient data verification and aggregation process. Then, a credibility-based cluster head election algorithm is designed, which reduces the communication overhead by clarifying the function distribution and building a hierarchical structure. Considering the good performance of the BLS threshold signature in large-scale applications, this paper combines it with distributed oracle technology and proposes a BLS threshold signature algorithm that supports share recovery in distributed oracles. The share recovery mechanism enables the proposed scheme to solve the key loss issue, and the setting of the threshold value enables the proposed scheme to complete signature aggregation with only a threshold number of oracles, making the scheme more robust. Finally, experimental results indicate that, by using the threshold signature technology and the cluster head election algorithm, our scheme effectively improves the execution efficiency of oracles and solves the problem of a single point of failure, leading to higher scalability and robustness.展开更多
This paper presents a novel approach to dynamic pricing and distributed energy management in virtual power plant(VPP)networks using multi-agent reinforcement learning(MARL).As the energy landscape evolves towards grea...This paper presents a novel approach to dynamic pricing and distributed energy management in virtual power plant(VPP)networks using multi-agent reinforcement learning(MARL).As the energy landscape evolves towards greater decentralization and renewable integration,traditional optimization methods struggle to address the inherent complexities and uncertainties.Our proposed MARL framework enables adaptive,decentralized decision-making for both the distribution system operator and individual VPPs,optimizing economic efficiency while maintaining grid stability.We formulate the problem as a Markov decision process and develop a custom MARL algorithm that leverages actor-critic architectures and experience replay.Extensive simulations across diverse scenarios demonstrate that our approach consistently outperforms baseline methods,including Stackelberg game models and model predictive control,achieving an 18.73%reduction in costs and a 22.46%increase in VPP profits.The MARL framework shows particular strength in scenarios with high renewable energy penetration,where it improves system performance by 11.95%compared with traditional methods.Furthermore,our approach demonstrates superior adaptability to unexpected events and mis-predictions,highlighting its potential for real-world implementation.展开更多
A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is proposed.An ambiguity set considering the inherent uncer...A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is proposed.An ambiguity set considering the inherent uncertainties of renewable energy sources(RESs)is constructed without requiring the full distribution knowledge of the uncertainties.The power balance chance constraint is reformulated within the framework of the distributionally robust optimization(DRO)approach.With the exchange of information and energy flow,each microgrid can achieve its local supply-demand balance.Furthermore,the closed-loop stability and recursive feasibility of the proposed algorithm are proved.The comparative results with other DSMPC methods show that a trade-off between robustness and economy can be achieved.展开更多
This article presents a distributed periodic eventtriggered(PET)optimal control scheme to achieve generation cost minimization and average bus voltage regulation in DC microgrids.In order to accommodate the generation...This article presents a distributed periodic eventtriggered(PET)optimal control scheme to achieve generation cost minimization and average bus voltage regulation in DC microgrids.In order to accommodate the generation constraints of the distributed generators(DGs),a virtual incremental cost is firstly designed,based on which an optimality condition is derived to facilitate the control design.To meet the discrete-time(DT)nature of modern control systems,the optimal controller is directly developed in the DT domain.Afterward,to reduce the communication requirement among the controllers,a distributed event-triggered mechanism is introduced for the DT optimal controller.The event-triggered condition is detected periodically and therefore naturally avoids the Zeno phenomenon.The closed-loop system stability is proved by the Lyapunov synthesis for switched systems.The generation cost minimization and average bus voltage regulation are obtained at the equilibrium point.Finally,switch-level microgrid simulations validate the performance of the proposed optimal controller.展开更多
基金The Science and Technology Project of the State Grid Corporation of China(Research and Demonstration of Loss Reduction Technology Based on Reactive Power Potential Exploration and Excitation of Distributed Photovoltaic-Energy Storage Converters:5400-202333241A-1-1-ZN).
文摘This paper presents a novel approach to economic dispatch in smart grids equipped with diverse energy devices.This method integrates features including photovoltaic(PV)systems,energy storage coupling,varied energy roles,and energy supply and demand dynamics.The systemmodel is developed by considering energy devices as versatile units capable of fulfilling various functionalities and playing multiple roles simultaneously.To strike a balance between optimality and feasibility,renewable energy resources are modeled with considerations for forecasting errors,Gaussian distribution,and penalty factors.Furthermore,this study introduces a distributed event-triggered surplus algorithm designed to address the economic dispatch problem by minimizing production costs.Rooted in surplus theory and finite time projection,the algorithm effectively rectifies network imbalances caused by directed graphs and addresses local inequality constraints.The algorithm greatly reduces the communication burden through event triggering mechanism.Finally,both theoretical proofs and numerical simulations verify the convergence and event-triggered nature of the algorithm.
文摘This paper introduces a novel fully distributed economic power dispatch(EPD)strategy for distribution networks,integrating dynamic tariffs.A two-layer model is proposed:the first layer comprises the physical power distribution network,including photovoltaic(PV)sources,wind turbine(WT)generators,energy storage systems(ESS),flexible loads(FLs),and other inflexible loads.The upper layer consists of agents dedicated to communication,calculation,and control tasks.Unlike previous EPD strategies,this approach incorporates dynamic tariffs derived from voltage constraints to ensure compliance with nodal voltage constraints.Addi-tionally,a fast distributed optimization algorithm with an event-triggered communication protocol has been developed to address the EPD problem effectively.Through mathematical and simulation analyses,the proposed algorithm's efficiency and rapid conver-gence capability are demonstrated.
基金supported in part by the National Key Research and Development Program of China(2021YFB1714800)the National Natural Science Foundation of China(62088101,61925303,62173034,U20B2073)+1 种基金the Natural Science Foundation of Chongqing(2021ZX4100027)the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)under Germanys Excellence Strategy—EXC 2075-390740016(468094890)。
文摘The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems(a.k.a.network systems).To this end,we start by putting forth a novel distributed event-triggering transmission strategy based on periodic sampling,under which a model-based stability criterion for the closed-loop network system is derived,by leveraging a discrete-time looped-functional approach.Marrying the model-based criterion with a data-driven system representation recently developed in the literature,a purely data-driven stability criterion expressed in the form of linear matrix inequalities(LMIs)is established.Meanwhile,the data-driven stability criterion suggests a means for co-designing the event-triggering coefficient matrix and the feedback control gain matrix using only some offline collected state-input data.Finally,numerical results corroborate the efficacy of the proposed distributed data-driven event-triggered network system(ETS)in cutting off data transmissions and the co-design procedure.
基金supported by the National Natural Science Foundation of China(Grant No.11701138)the Natural Science Foundation of Hebei Province,China(Grant Nos.F2017202009 and F2018202075)
文摘The distributed event-triggered optimization problem for multiple nonholonomic robots has been studied to minimize the global battery energy consumption. Each robot possesses its own cost function which depends on the state of the hand position and represents battery energy consumption. By coordinate transformation, the dynamics of the hand positions can be formulated into two groups of first-order integrators. Then the distributed event-triggered optimization algorithm is designed such that the states of robots' hand positions exponentially converge to the optimizer of the global cost function.Meanwhile, the velocity and orientation of each robot are ensured to reach zero and a certain constant, respectively. Moreover, the inter-execution time is lower bounded and the Zeno behavior is therefore naturally avoided. Numerical simulations show the effectiveness of the proposed algorithm.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61203147,61374047,and 61403168)
文摘This paper investigates the consensus tracking problems of second-order multi-agent systems with a virtual leader via event-triggered control. A novel distributed event-triggered transmission scheme is proposed, which is intermittently examined at constant sampling instants. Only partial neighbor information and local measurements are required for event detection. Then the corresponding event-triggered consensus tracking protocol is presented to guarantee second-order multi-agent systems to achieve consensus tracking. Numerical simulations are given to illustrate the effectiveness of the proposed strategy.
基金Project supported by the National Natural Science Foundation of China(Grant No.11701138)the Natural Science Foundation of Hebei Province,China(Grant Nos.F2017202009 and F2018202075)
文摘This paper investigates the time-varying formation problem for general linear multi-agent systems using distributed event-triggered control strategy.Different from the previous works,to achieve the desired time-varying formation,a distributed control scheme is designed in an event-triggered way,in which for each agent the controller is triggered only at its own event times.The interaction topology among agents is assumed to be switching.The common Lyapunov function as well as Riccati inequality is applied to solve the time-varying formation problem.Moreover,the Zeno behavior of triggering time sequences can be excluded for each agent.Finally,a simulation example is presented to illustrate the effectiveness of the theoretical results.
基金supported in part by the National Key R&D Program of China(No.2023YFB4704400)in part by the National Natural Science Foundation of China(Nos.U23B2036,U2013201).
文摘In this paper,a distributed Event-Triggered(ET)collision avoidance coordinated control for Quadrotor Unmanned Aerial Vehicles(QUAVs)is proposed based on Virtual Tubes(VTs)with flexible boundaries in the presence of unknown external disturbances.Firstly,VTs are constructed for each QUAV,and the QUAV is restricted into the corresponding VT by the artificial potential field,which is distributed around the boundary of the VT.Thus,the collisions between QUAVs are avoided.Besides,the boundaries of the VTs are flexible by the modification signals,which are generated by the self-regulating auxiliary systems,to make the repulsive force smaller and give more buffer space for QUAVs without collision.Then,a novel ET mechanism is designed by introducing the concept of prediction to the traditional fixed threshold ET mechanism.Furthermore,a disturbance observer is proposed to deal with the adverse effects of the unknown external disturbance.On this basis,a distributed ET collision avoidance coordinated controller is proposed.Then,the proposed controller is quantized by the hysteresis uniform quantizer and then sent to the actuator only at the ET instants.The boundedness of the closed-loop signals is verified by the Lyapunov method.Finally,simulation and experimental results are performed to demonstrate the superiority of the proposed control method.
基金supported by the National Key Research and Development Program of China(Basic Research Class)(2017YFB0903000)the National Natural Science Foundation of China(U1909201).
文摘For islanded microgrids(MGs),distributed control is regarded as a preferred alternative to centralized control for the frequency restoration of MGs.However,distributed control with successive communication restricts the efficiency and resilience of the control system.To address this issue,this paper proposes a distributed event-triggered control strategy for the frequency secondary control in islanded MGs.The proposed event-triggered control is Zeno behavior free and enables each DG to update and propagate its state to neighboring DGs only when a specific“event”occurs,which significantly reduces the communication burden.Compared with the existing event-triggered control,a trigger condition checking period of the proposed event-triggered control is provided to reduce the computation burden when checking the trigger condition.Furthermore,using the aperiodicity and intermittent properties of the communication,a simple detection principle is proposed to detect and isolate the compromised communication links in a timely and economic fashion,which improves the resilience of the system against FDI attacks.Finally,the control effectiveness of the proposed control scheme is validated by the simulation results of the tests on an MG with 4 DGs.
基金Project supported by the National Natural Science Foundation of China (Grant No.62073045)。
文摘We develop a policy of observer-based dynamic event-triggered state feedback control for distributed parameter systems over a mobile sensor-plus-actuator network.It is assumed that the mobile sensing devices that provide spatially averaged state measurements can be used to improve state estimation in the network.For the purpose of decreasing the update frequency of controller and unnecessary sampled data transmission, an efficient dynamic event-triggered control policy is constructed.In an event-triggered system, when an error signal exceeds a specified time-varying threshold, it indicates the occurrence of a typical event.The global asymptotic stability of the event-triggered closed-loop system and the boundedness of the minimum inter-event time can be guaranteed.Based on the linear quadratic optimal regulator, the actuator selects the optimal displacement only when an event occurs.A simulation example is finally used to verify that the effectiveness of such a control strategy can enhance the system performance.
基金supported by the Beijing Municipal Science&Technology Commission China(No.Z19111000270000)the National Natural Science Foundation of China(Nos.62203050,51774042).
文摘Within the context of ground-air cooperation,the distributed formation trajectory tracking control problems for the Heterogeneous Multi-Agent Systems(HMASs)is studied.First,considering external disturbances and model uncertainties,a graph theory-based formation control protocol is designed for the HMASs consisting of Unmanned Aerial Vehicles(UAVs)and Unmanned Ground Vehicles(UGVs).Subsequently,a formation trajectory tracking control strategy employing adaptive Fractional-Order Sliding Mode Control(FOSMC)method is developed,and a Feedback Multilayer Fuzzy Neural Network(FMFNN)is introduced to estimate the lumped uncertainties.This approach empowers HMASs to adaptively follow the expected trajectory and adopt the designated formation configuration,even in the presence of various uncertainties.Additionally,an event-triggered mechanism is incorporated into the controller to reduce the update frequency of the controller and minimize the communication exchange among the agents,and the absence of Zeno behavior is rigorously demonstrated by an integral inequality analysis.Finally,to confirm the effectiveness of the proposed formation control protocol,some numerical simulations are presented.
基金supported by the National Natural Science Foundation of China(62473316,62073269)the Natural Science Foundation of Chongqing,China(CSTB2022NSCQ-MSX0963)+1 种基金Guangdong Basic and Applied Basic Research Foundation(2023A1515011220)Aeronautical Science Foundation of China(2020Z034053002)。
文摘The article investigates the optimal energy management(OEM)problem for microgrids.To figure out the problem in fixed time and alleviate communication load with limited resources,this article devises a novel fixed-time stability lemma and an event-triggered(ET)fixed-time distributed OEM approach.Using Lyapunov stability theory,the distributed approach has been proven to converge in fixed time and the upper bound on convergence time can be derived without dependence on the initial states.The dynamic ET method is raised to dynamically adjust the triggering threshold and reduce communication redundancy.In addition,Zeno behavior is avoided.Simulations are given to show the effectiveness and advantage of the designed distributed OEM method.
基金Project supported by the National Natural Science Foundation of China(Nos.61873128 and 61673219)the Jiangsu Key Research and Development Plan,China(No.BE2018004-3)。
文摘We study the contain men t cont rol problem for high-order het erogeneous nonlinear multi-age nt systems under distributed event-triggered schemes.To achieve the containment control objective and reduce communication consumption among agents,a distributed event-triggered control scheme is proposed by applying the backstepping method,Lyapunov functional approach,and neural networks.Then,the results are extended to the self-triggered control case to avoid continuous monitoring of state errors.The developed protocols and triggered rules ensure that the output for each follower converges to the convex hull spanned by multi-leader signals within a bounded error.In addition,no agent exhibits Zeno behavior.Two numerical simulations are finally presen ted to verify the correctness of the obtained results.
基金funded by the National Natural Science Foundation of China(52167013)the Key Program of Natural Science Foundation of Gansu Province(24JRRA225)Natural Science Foundation of Gansu Province(23JRRA891).
文摘After a century of relative stability in the electricity sector,the widespread adoption of distributed energy resources,along with recent advancements in computing and communication technologies,has fundamentally altered how energy is consumed,traded,and utilized.This change signifies a crucial shift as the power system evolves from its traditional hierarchical organization to a more decentralized approach.At the heart of this transformation are innovative energy distribution models,like peer-to-peer(P2P)sharing,which enable communities to collaboratively manage their energy resources.The effectiveness of P2P sharing not only improves the economic prospects for prosumers,who generate and consume energy,but also enhances energy resilience and sustainability.This allows communities to better leverage local resources while fostering a sense of collective responsibility and collaboration in energy management.However,there is still no extensive implementation of such sharing models in today’s electricitymarkets.Research on distributed energy P2P trading is still in the exploratory stage,and it is particularly important to comprehensively understand and analyze the existing distributed energy P2P trading market.This paper contributes with an overview of the P2P markets that starts with the network framework,market structure,technical approach for trading mechanism,and blockchain technology,moving to the outlook in this field.
基金supported in part by the National Key Research and Development Program of China under Grant No.2021YFF0901300in part by the National Natural Science Foundation of China under Grant Nos.62173076 and 72271048.
文摘The distributed permutation flow shop scheduling problem(DPFSP)has received increasing attention in recent years.The iterated greedy algorithm(IGA)serves as a powerful optimizer for addressing such a problem because of its straightforward,single-solution evolution framework.However,a potential draw-back of IGA is the lack of utilization of historical information,which could lead to an imbalance between exploration and exploitation,especially in large-scale DPFSPs.As a consequence,this paper develops an IGA with memory and learning mechanisms(MLIGA)to efficiently solve the DPFSP targeted at the mini-malmakespan.InMLIGA,we incorporate a memory mechanism to make a more informed selection of the initial solution at each stage of the search,by extending,reconstructing,and reinforcing the information from previous solutions.In addition,we design a twolayer cooperative reinforcement learning approach to intelligently determine the key parameters of IGA and the operations of the memory mechanism.Meanwhile,to ensure that the experience generated by each perturbation operator is fully learned and to reduce the prior parameters of MLIGA,a probability curve-based acceptance criterion is proposed by combining a cube root function with custom rules.At last,a discrete adaptive learning rate is employed to enhance the stability of the memory and learningmechanisms.Complete ablation experiments are utilized to verify the effectiveness of the memory mechanism,and the results show that this mechanism is capable of improving the performance of IGA to a large extent.Furthermore,through comparative experiments involving MLIGA and five state-of-the-art algorithms on 720 benchmarks,we have discovered that MLI-GA demonstrates significant potential for solving large-scale DPFSPs.This indicates that MLIGA is well-suited for real-world distributed flow shop scheduling.
基金supported by Guangdong Major Project of Basic and Applied Basic Research(Grant No.2023B0303000016)the National Natural Science Foundation of China(Grant No.U21A20487)+5 种基金Shenzhen Technology Project(Grant Nos.JCYJ20220818101206014,JCYJ20220818101211025)the CAS Key Technology Talent Program,the National Outstanding Youth Talents Support Program(Grant No.61822304)Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0100)Shanghai Municipal Commission of Science and Technology Project(Grant No.19511132101)the Projects of Major International(Regional)Joint Research Program of NSFC(Grant No.61720106011)the National Natural Science Foundation of China(Grant No.62372440)。
文摘This article explores the leader-following consensus tracking(LFCK)control issues of multi-agent systems(MASs)in the presence of external disturbances and general directed fixed communication topology.Its purpose is to enable all follower agents to achieve consensus tracking for the leader agent.Firstly,this article introduces an extended state observer for estimating each follower agent's unknown state and external disturbance.Subsequently,on the basis of the above-extended state observer and a dynamic event-triggered strategy,a distributed consensus tracking control protocol with disturbances restraint is developed,which can reduce the MAS's update frequency on the premise of ensuring the control protocol's effectiveness.Furthermore,the MAS's stability and the absence of Zeno behavior are analyzed and proved by the established Lyapunov functional and linear matrix inequality theory.Finally,the validity and feasibility of the proposed approach are validated through a group of comparative numerical simulation experiments.
基金funded by the Jilin Province Science and Technology Development Plan Project(20230101344JC).
文摘A centralized-distributed scheduling strategy for distribution networks based on multi-temporal and hierarchical cooperative game is proposed to address the issues of difficult operation control and energy optimization interaction in distribution network transformer areas,as well as the problem of significant photovoltaic curtailment due to the inability to consume photovoltaic power locally.A scheduling architecture combiningmulti-temporal scales with a three-level decision-making hierarchy is established:the overall approach adopts a centralized-distributed method,analyzing the operational characteristics and interaction relationships of the distribution network center layer,cluster layer,and transformer area layer,providing a“spatial foundation”for subsequent optimization.The optimization process is divided into two stages on the temporal scale:in the first stage,based on forecasted electricity load and demand response characteristics,time-of-use electricity prices are utilized to formulate day-ahead optimization strategies;in the second stage,based on the charging and discharging characteristics of energy storage vehicles and multi-agent cooperative game relationships,rolling electricity prices and optimal interactive energy solutions are determined among clusters and transformer areas using the Nash bargaining theory.Finally,a distributed optimization algorithm using the bisection method is employed to solve the constructed model.Simulation results demonstrate that the proposed optimization strategy can facilitate photovoltaic consumption in the distribution network and enhance grid economy.
基金supported by the National Natural Science Foundation of China(Grant No.62102449)the Central Plains Talent Program under Grant No.224200510003.
文摘With the increasing popularity of blockchain applications, the security of data sources on the blockchain is gradually receiving attention. Providing reliable data for the blockchain safely and efficiently has become a research hotspot, and the security of the oracle responsible for providing reliable data has attracted much attention. The most widely used centralized oracles in blockchain, such as Provable and Town Crier, all rely on a single oracle to obtain data, which suffers from a single point of failure and limits the large-scale development of blockchain. To this end, the distributed oracle scheme is put forward, but the existing distributed oracle schemes such as Chainlink and Augur generally have low execution efficiency and high communication overhead, which leads to their poor applicability. To solve the above problems, this paper proposes a trusted distributed oracle scheme based on a share recovery threshold signature. First, a data verification method of distributed oracles is designed based on threshold signature. By aggregating the signatures of oracles, data from different data sources can be mutually verified, leading to a more efficient data verification and aggregation process. Then, a credibility-based cluster head election algorithm is designed, which reduces the communication overhead by clarifying the function distribution and building a hierarchical structure. Considering the good performance of the BLS threshold signature in large-scale applications, this paper combines it with distributed oracle technology and proposes a BLS threshold signature algorithm that supports share recovery in distributed oracles. The share recovery mechanism enables the proposed scheme to solve the key loss issue, and the setting of the threshold value enables the proposed scheme to complete signature aggregation with only a threshold number of oracles, making the scheme more robust. Finally, experimental results indicate that, by using the threshold signature technology and the cluster head election algorithm, our scheme effectively improves the execution efficiency of oracles and solves the problem of a single point of failure, leading to higher scalability and robustness.
基金supported by the Science and Technology Project of State Grid Sichuan Electric Power Company Chengdu Power Supply Company under Grant No.521904240005.
文摘This paper presents a novel approach to dynamic pricing and distributed energy management in virtual power plant(VPP)networks using multi-agent reinforcement learning(MARL).As the energy landscape evolves towards greater decentralization and renewable integration,traditional optimization methods struggle to address the inherent complexities and uncertainties.Our proposed MARL framework enables adaptive,decentralized decision-making for both the distribution system operator and individual VPPs,optimizing economic efficiency while maintaining grid stability.We formulate the problem as a Markov decision process and develop a custom MARL algorithm that leverages actor-critic architectures and experience replay.Extensive simulations across diverse scenarios demonstrate that our approach consistently outperforms baseline methods,including Stackelberg game models and model predictive control,achieving an 18.73%reduction in costs and a 22.46%increase in VPP profits.The MARL framework shows particular strength in scenarios with high renewable energy penetration,where it improves system performance by 11.95%compared with traditional methods.Furthermore,our approach demonstrates superior adaptability to unexpected events and mis-predictions,highlighting its potential for real-world implementation.
基金Supported by the National Natural Science Foundation of China(No.U24B20156)the National Defense Basic Scientific Research Program of China(No.JCKY2021204B051)the National Laboratory of Space Intelligent Control of China(Nos.HTKJ2023KL502005 and HTKJ2024KL502007)。
文摘A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is proposed.An ambiguity set considering the inherent uncertainties of renewable energy sources(RESs)is constructed without requiring the full distribution knowledge of the uncertainties.The power balance chance constraint is reformulated within the framework of the distributionally robust optimization(DRO)approach.With the exchange of information and energy flow,each microgrid can achieve its local supply-demand balance.Furthermore,the closed-loop stability and recursive feasibility of the proposed algorithm are proved.The comparative results with other DSMPC methods show that a trade-off between robustness and economy can be achieved.
基金supported by the U.S.Office of Naval Research(N00014-21-1-2175)。
文摘This article presents a distributed periodic eventtriggered(PET)optimal control scheme to achieve generation cost minimization and average bus voltage regulation in DC microgrids.In order to accommodate the generation constraints of the distributed generators(DGs),a virtual incremental cost is firstly designed,based on which an optimality condition is derived to facilitate the control design.To meet the discrete-time(DT)nature of modern control systems,the optimal controller is directly developed in the DT domain.Afterward,to reduce the communication requirement among the controllers,a distributed event-triggered mechanism is introduced for the DT optimal controller.The event-triggered condition is detected periodically and therefore naturally avoids the Zeno phenomenon.The closed-loop system stability is proved by the Lyapunov synthesis for switched systems.The generation cost minimization and average bus voltage regulation are obtained at the equilibrium point.Finally,switch-level microgrid simulations validate the performance of the proposed optimal controller.
