This paper highlights the utilization of parallel control and adaptive dynamic programming(ADP) for event-triggered robust parallel optimal consensus control(ETRPOC) of uncertain nonlinear continuous-time multiagent s...This paper highlights the utilization of parallel control and adaptive dynamic programming(ADP) for event-triggered robust parallel optimal consensus control(ETRPOC) of uncertain nonlinear continuous-time multiagent systems(MASs).First, the parallel control system, which consists of a virtual control variable and a specific auxiliary variable obtained from the coupled Hamiltonian, allows general systems to be transformed into affine systems. Of interest is the fact that the parallel control technique's introduction provides an unprecedented perspective on eliminating the negative effects of disturbance. Then, an eventtriggered mechanism is adopted to save communication resources while ensuring the system's stability. The coupled HamiltonJacobi(HJ) equation's solution is approximated using a critic neural network(NN), whose weights are updated in response to events. Furthermore, theoretical analysis reveals that the weight estimation error is uniformly ultimately bounded(UUB). Finally,numerical simulations demonstrate the effectiveness of the developed ETRPOC method.展开更多
The traditional Practical Byzantine Fault Tolerance(PBFT)approach suffers from three critical deficiencies:arbitrary primary node election,excessive network transmission overhead,coupled with the absence of node incen...The traditional Practical Byzantine Fault Tolerance(PBFT)approach suffers from three critical deficiencies:arbitrary primary node election,excessive network transmission overhead,coupled with the absence of node incentive mechanisms.To address these issues,this study proposes a refined PBFT strategy utilizing dual scoring(Double Scoring Practical Byzantine Fault Tolerance,DS-PBFT).The algorithm innovatively combines hardware performance evaluation with a dual-dimensional node scoring system.The algorithm first employs bucket sorting technology to quantitatively evaluate node hardware resources,followed by constructing a comprehensive scoring model through credit values and recommendation values.According to the scoring outcomes,the framework hierarchically divides nodes into primary node,follower node and backup nodes groups in a 1:4:5 ratio,substantially decreasing the quantity of nodes involved in consensus.Additionally,this approach streamlines the Commit-Reply stages within the consistency protocol,substantially reducing communication overhead.Experimental validation demonstrates that DS-PBFT maintains security while achieving notable improvements in consensus efficiency,significant reductions in communication costs,and enhanced defense capabilities against malicious nodes.展开更多
This paper addresses the distributed optimization problems of multi-agent systems using a distributed hybrid impulsive protocol.The objective is to ensure the agents achieve the state consensus and optimize the aggreg...This paper addresses the distributed optimization problems of multi-agent systems using a distributed hybrid impulsive protocol.The objective is to ensure the agents achieve the state consensus and optimize the aggregate objective functions assigned for each agent with distributed manner. We establish two criteria related to the optimality condition and the impulsive gain upper estimation, and propose a distributed hybrid impulsive optimal protocol, which includes two terms: the local averaging term in the continuous interval and the term involving the gradient information at impulsive instants. The simulation results show that the optimal consensus can be realized under the distributed hybrid impulsive optimization algorithm.展开更多
This study explores a new robust consensus control strategy for uncertain multiagent systems and provides an event-based solution to adaptive dynamic programming(ADP)based optimal control.Rather than the control funct...This study explores a new robust consensus control strategy for uncertain multiagent systems and provides an event-based solution to adaptive dynamic programming(ADP)based optimal control.Rather than the control function,the feedback system established symmetrical to the physical system allows the optimal consensus control issue to be handled by the optimal control protocol of an augmented affine system.The feedback system focuses on an auxiliary variable formed in light of the optimality principle and the virtual control input built on a critic neural network(NN).Analysis reveals that the auxiliary variable benefits from decreasing the influence of uncertainty on control performance,while the proposed approach is implemented with fewer communication resources since the critic NN is updated as events occur.Finally,evidence from simulation findings validates the theoretical results.展开更多
Multi-agent reinforcement learning(MARL) has long been a significant research topic in both machine learning and control systems. Recent development of(single-agent) deep reinforcement learning has created a resurgenc...Multi-agent reinforcement learning(MARL) has long been a significant research topic in both machine learning and control systems. Recent development of(single-agent) deep reinforcement learning has created a resurgence of interest in developing new MARL algorithms, especially those founded on theoretical analysis. In this paper, we review recent advances on a sub-area of this topic: decentralized MARL with networked agents.In this scenario, multiple agents perform sequential decision-making in a common environment, and without the coordination of any central controller, while being allowed to exchange information with their neighbors over a communication network. Such a setting finds broad applications in the control and operation of robots, unmanned vehicles, mobile sensor networks, and the smart grid. This review covers several of our research endeavors in this direction, as well as progress made by other researchers along the line. We hope that this review promotes additional research efforts in this exciting yet challenging area.展开更多
基金supported in part by the National Key Research and Development Program of China(2021YFE0206100)the National Natural Science Foundation of China(62425310,62073321)+2 种基金the National Defense Basic Scientific Research Program(JCKY2019203C029,JCKY2020130C025)the Science and Technology Development FundMacao SAR(FDCT-22-009-MISE,0060/2021/A2,0015/2020/AMJ)
文摘This paper highlights the utilization of parallel control and adaptive dynamic programming(ADP) for event-triggered robust parallel optimal consensus control(ETRPOC) of uncertain nonlinear continuous-time multiagent systems(MASs).First, the parallel control system, which consists of a virtual control variable and a specific auxiliary variable obtained from the coupled Hamiltonian, allows general systems to be transformed into affine systems. Of interest is the fact that the parallel control technique's introduction provides an unprecedented perspective on eliminating the negative effects of disturbance. Then, an eventtriggered mechanism is adopted to save communication resources while ensuring the system's stability. The coupled HamiltonJacobi(HJ) equation's solution is approximated using a critic neural network(NN), whose weights are updated in response to events. Furthermore, theoretical analysis reveals that the weight estimation error is uniformly ultimately bounded(UUB). Finally,numerical simulations demonstrate the effectiveness of the developed ETRPOC method.
文摘The traditional Practical Byzantine Fault Tolerance(PBFT)approach suffers from three critical deficiencies:arbitrary primary node election,excessive network transmission overhead,coupled with the absence of node incentive mechanisms.To address these issues,this study proposes a refined PBFT strategy utilizing dual scoring(Double Scoring Practical Byzantine Fault Tolerance,DS-PBFT).The algorithm innovatively combines hardware performance evaluation with a dual-dimensional node scoring system.The algorithm first employs bucket sorting technology to quantitatively evaluate node hardware resources,followed by constructing a comprehensive scoring model through credit values and recommendation values.According to the scoring outcomes,the framework hierarchically divides nodes into primary node,follower node and backup nodes groups in a 1:4:5 ratio,substantially decreasing the quantity of nodes involved in consensus.Additionally,this approach streamlines the Commit-Reply stages within the consistency protocol,substantially reducing communication overhead.Experimental validation demonstrates that DS-PBFT maintains security while achieving notable improvements in consensus efficiency,significant reductions in communication costs,and enhanced defense capabilities against malicious nodes.
基金supported in part by the National Key Research and Development Program of China (Grant No. 2020YFA0714300)the National Natural Science Foundation of China (Grant Nos. 61833005 and 62003084)+1 种基金the Fundamental Research Funds for the Central Universitiesthe Jiangsu Provincial Key Laboratory of Networked Collective Intelligence (Grant No. BM2017002)。
文摘This paper addresses the distributed optimization problems of multi-agent systems using a distributed hybrid impulsive protocol.The objective is to ensure the agents achieve the state consensus and optimize the aggregate objective functions assigned for each agent with distributed manner. We establish two criteria related to the optimality condition and the impulsive gain upper estimation, and propose a distributed hybrid impulsive optimal protocol, which includes two terms: the local averaging term in the continuous interval and the term involving the gradient information at impulsive instants. The simulation results show that the optimal consensus can be realized under the distributed hybrid impulsive optimization algorithm.
基金supported by the National Key R&D Program of China(Nos.2024YFB4709100 and 2021YFE0206100)the National Natural Science Foundation of China(Nos.62425310 and 62073321)+1 种基金the National Defense Basic Scientific Research Program(Nos.JCKY2019203C029 and JCKY2020130C025)the Science and Technology Development Fund,Macao SAR(Nos.FDCT-22-009-MISE,0060/2021/A2,and 0015/2020/AMJ).
文摘This study explores a new robust consensus control strategy for uncertain multiagent systems and provides an event-based solution to adaptive dynamic programming(ADP)based optimal control.Rather than the control function,the feedback system established symmetrical to the physical system allows the optimal consensus control issue to be handled by the optimal control protocol of an augmented affine system.The feedback system focuses on an auxiliary variable formed in light of the optimality principle and the virtual control input built on a critic neural network(NN).Analysis reveals that the auxiliary variable benefits from decreasing the influence of uncertainty on control performance,while the proposed approach is implemented with fewer communication resources since the critic NN is updated as events occur.Finally,evidence from simulation findings validates the theoretical results.
基金Project supported in part by the US Army Research Laboratory(ARL) Cooperative Agreement (No. W911NF-17-2-0196)the Air Force Office of Scientific Research (AFOSR)Grant (No. FA9550-19-1-0353)。
文摘Multi-agent reinforcement learning(MARL) has long been a significant research topic in both machine learning and control systems. Recent development of(single-agent) deep reinforcement learning has created a resurgence of interest in developing new MARL algorithms, especially those founded on theoretical analysis. In this paper, we review recent advances on a sub-area of this topic: decentralized MARL with networked agents.In this scenario, multiple agents perform sequential decision-making in a common environment, and without the coordination of any central controller, while being allowed to exchange information with their neighbors over a communication network. Such a setting finds broad applications in the control and operation of robots, unmanned vehicles, mobile sensor networks, and the smart grid. This review covers several of our research endeavors in this direction, as well as progress made by other researchers along the line. We hope that this review promotes additional research efforts in this exciting yet challenging area.
