In this paper,an asymmetric bipartite consensus problem for the nonlinear multi-agent systems with cooperative and antagonistic interactions is studied under the event-triggered mechanism.For the agents described by a...In this paper,an asymmetric bipartite consensus problem for the nonlinear multi-agent systems with cooperative and antagonistic interactions is studied under the event-triggered mechanism.For the agents described by a structurally balanced signed digraph,the asymmetric bipartite consensus objective is firstly defined,assigning the agents'output to different signs and module values.Considering with the completely unknown dynamics of the agents,a novel event-triggered model-free adaptive bipartite control protocol is designed based on the agents'triggered outputs and an equivalent compact form data model.By utilizing the Lyapunov analysis method,the threshold of the triggering condition is obtained.Subsequently,the asymptotic convergence of the tracking error is deduced and a sufficient condition is obtained based on the contraction mapping principle.Finally,the simulation example further demonstrates the effectiveness of the protocol.展开更多
This paper considers the human-in-the-loop leader-following consensus control problem of multi-agent systems(MASs)with unknown matched nonlinear functions and actuator faults.It is assumed that a human operator contro...This paper considers the human-in-the-loop leader-following consensus control problem of multi-agent systems(MASs)with unknown matched nonlinear functions and actuator faults.It is assumed that a human operator controls the MASs via sending the command signal to a non-autonomous leader which generates the desired trajectory.Moreover,the leader’s input is nonzero and not available to all followers.By using neural networks and fault estimators to approximate unknown nonlinear dynamics and identify the actuator faults,respectively,the neighborhood observer-based neural fault-tolerant controller with dynamic coupling gains is designed.It is proved that the state of each follower can synchronize with the leader’s state under a directed graph and all signals in the closed-loop system are guaranteed to be cooperatively uniformly ultimately bounded.Finally,simulation results are presented for verifying the effectiveness of the proposed control method.展开更多
This paper investigates the event-triggered security consensus problem for nonlinear multi-agent systems(MASs)under denial-of-service(Do S)attacks over an undirected graph.A novel adaptive memory observer-based anti-d...This paper investigates the event-triggered security consensus problem for nonlinear multi-agent systems(MASs)under denial-of-service(Do S)attacks over an undirected graph.A novel adaptive memory observer-based anti-disturbance control scheme is presented to improve the observer accuracy by adding a buffer for the system output measurements.Meanwhile,this control scheme can also provide more reasonable control signals when Do S attacks occur.To save network resources,an adaptive memory event-triggered mechanism(AMETM)is also proposed and Zeno behavior is excluded.It is worth mentioning that the AMETM's updates do not require global information.Then,the observer and controller gains are obtained by using the linear matrix inequality(LMI)technique.Finally,simulation examples show the effectiveness of the proposed control scheme.展开更多
This paper investigates the consensus problem of second-order nonlinear multi-agent systems (MASs) via the sliding mode control (SMC) approach. The velocity of each agent is assumed to be unmeasurable. A second-order ...This paper investigates the consensus problem of second-order nonlinear multi-agent systems (MASs) via the sliding mode control (SMC) approach. The velocity of each agent is assumed to be unmeasurable. A second-order sliding mode observer is designed to estimate the velocity. Then a distributed discontinuous control law based on first-order SMC is presented to solve the consensus problem. Moreover, to overcome the chatting problem, two controllers based on the boundary layer method and the super-twisting algorithm respectively are presented. It is shown that the MASs will achieve consensus under some given conditions. Some examples are provided to demonstrate the effectiveness of the proposed control laws.展开更多
For a class of high-order nonlinear multi-agent systems with input hysteresis,an adaptive consensus output-feedback quantized control scheme with full state constraints is investigated.The major properties of the prop...For a class of high-order nonlinear multi-agent systems with input hysteresis,an adaptive consensus output-feedback quantized control scheme with full state constraints is investigated.The major properties of the proposed control scheme are:1)According to the different hysteresis input characteristics of each agent in the multi-agent system,a hysteresis quantization inverse compensator is designed to eliminate the influence of hysteresis characteristics on the system while ensuring that the quantized signal maintains the desired value.2)A barrier Lyapunov function is introduced for the first time in the hysteretic multi-agent system.By constructing state constraint control strategy for the hysteretic multi-agent system,it ensures that all the states of the system are always maintained within a predetermined range.3)The designed adaptive consensus output-feedback quantization control scheme allows the hysteretic system to have unknown parameters and unknown disturbance,and ensures that the input signal transmitted between agents is the quantization value,and the introduced quantizer is implemented under the condition that only its sector bound property is required.The stability analysis has proved that all signals of the closed-loop are semi-globally uniformly bounded.The Star Sim hardware-in-the-loop simulation certificates the effectiveness of the proposed adaptive quantized control scheme.展开更多
This paper investigates the distributed fault-tolerant containment control(FTCC)problem of nonlinear multi-agent systems(MASs)under a directed network topology.The proposed control framework which is independent on th...This paper investigates the distributed fault-tolerant containment control(FTCC)problem of nonlinear multi-agent systems(MASs)under a directed network topology.The proposed control framework which is independent on the global information about the communication topology consists of two layers.Different from most existing distributed fault-tolerant control(FTC)protocols where the fault in one agent may propagate over network,the developed control method can eliminate the phenomenon of fault propagation.Based on the hierarchical control strategy,the FTCC problem with a directed graph can be simplified to the distributed containment control of the upper layer and the fault-tolerant tracking control of the lower layer.Finally,simulation results are given to demonstrate the effectiveness of the proposed control protocol.展开更多
We investigate the third-order leader-following consensus problem of nonlinear multi-agent systems in undirected network topologies.Based on graph theory and Lyapunov stability theory,the adaptive control method is em...We investigate the third-order leader-following consensus problem of nonlinear multi-agent systems in undirected network topologies.Based on graph theory and Lyapunov stability theory,the adaptive control method is employed to achieve leader-following consensus in an undirected network of agents with nonlinear third-order dynamics against the perturbations.Simulation examples validate the correctness of the results and show that the control gains have a great influence on the convergence performance of errors for a short time.展开更多
This paper investigates adaptive containment control for a class of fractional-order multi-agent systems(FOMASs)with time-varying parameters and disturbances.By using the bounded estimation method,the difficulty gener...This paper investigates adaptive containment control for a class of fractional-order multi-agent systems(FOMASs)with time-varying parameters and disturbances.By using the bounded estimation method,the difficulty generated by the timevarying parameters and disturbances is overcome.The command filter is introduced to solve the complexity problem inherent in adaptive backstepping control.Meanwhile,in order to eliminate the effect of filter errors,a novel distributed error compensating scheme is constructed,in which only the local information from the neighbor agents is utilized.Then,a distributed adaptive containment control scheme for FOMASs is developed based on backstepping to guarantee that the outputs of all the followers are steered to the convex hull spanned by the leaders.Based on the extension of Barbalat's lemma to fractional-order integrals,it can be proven that the containment errors and the compensating signals have asymptotic convergence.Finally,three simulation examples are given to show the feasibility and effectiveness of the proposed control method.展开更多
In this paper,the problems of robust consensus tracking control for the second-order multi-agent system with uncertain model parameters and nonlinear disturbances are considered.An adaptive control strategy is propose...In this paper,the problems of robust consensus tracking control for the second-order multi-agent system with uncertain model parameters and nonlinear disturbances are considered.An adaptive control strategy is proposed to smooth the agent’s trajectory,and the neural network is constructed to estimate the system’s unknown components.The consensus conditions are demonstrated for tracking a leader with nonlinear dynamics under an adaptive control algorithm in the absence of model uncertainties.Then,the results are extended to the system with unknown time-varying disturbances by applying the neural network estimation to compensating for the uncertain parts of the agents’models.Update laws are designed based on the Lyapunov function terms to ensure the effectiveness of robust control.Finally,the theoretical results are verified by numerical simulations,and a comparative experiment is conducted,showing that the trajectories generated by the proposed method exhibit less oscillation and converge faster.展开更多
This paper investigates the consensus-based formation control problem for multi-agent systems with unknown nonlinear dynamics.To achieve the desired formation,we propose two formation controllers to achieve the desire...This paper investigates the consensus-based formation control problem for multi-agent systems with unknown nonlinear dynamics.To achieve the desired formation,we propose two formation controllers to achieve the desired formation,one based on system states and the other on system outputs.The proposed controllers utilize adaptive gains to avoid global information and neural networks to estimate and compensate for nonlinearities.The proposed event-triggered schemes avoid continuous communication among agents and exclude the Zeno behavior.Stability analysis reveals that formation errors are bounded,and numerical simulations are used to validate the effectiveness of the proposed approaches.展开更多
To solve the synchronization and tracking problems,a cooperative control scheme is proposed for a class of higher-order multi-input and multi-output(MIMO)nonlinear multi-agent systems(MASs)subjected to uncertainties a...To solve the synchronization and tracking problems,a cooperative control scheme is proposed for a class of higher-order multi-input and multi-output(MIMO)nonlinear multi-agent systems(MASs)subjected to uncertainties and external disturbances.First,coupled relationships among Laplace matrix,leader-following adjacency matrix and consensus error are analyzed based on undirected graph.Furthermore,nonlinear disturbance observers(NDOs)are designed to estimate compounded disturbances in MASs,and a distributed cooperative anti-disturbance control protocol is proposed for high-order MIMO nonlinear MASs based on the outputs of NDOs and dynamic surface control approach.Finally,the feasibility and effectiveness of the proposed scheme are proven based on Lyapunov stability theory and simulation experiments.展开更多
To address fixed-time consensus problems of a class of leader-follower second-order nonlinear multi-agent systems with uncertain external disturbances,the event-triggered fixed-time consensus protocol is proposed.Firs...To address fixed-time consensus problems of a class of leader-follower second-order nonlinear multi-agent systems with uncertain external disturbances,the event-triggered fixed-time consensus protocol is proposed.First,the virtual velocity is designed based on the backstepping control method to achieve the system consensus and the bound on convergence time only depending on the system parameters.Second,an event-triggered mechanism is presented to solve the problem of frequent communication between agents,and triggered condition based on state information is given for each follower.It is available to save communication resources,and the Zeno behaviors are excluded.Then,the delay and switching topologies of the system are also discussed.Next,the system stabilization is analyzed by Lyapunov stability theory.Finally,simulation results demonstrate the validity of the presented method.展开更多
This paper studies the dynamic event-triggered leader-follower consensus of nonlinear multi-agent systems(MASs)under directed weighted graph containing a directed spanning tree,and also considers the effects of distur...This paper studies the dynamic event-triggered leader-follower consensus of nonlinear multi-agent systems(MASs)under directed weighted graph containing a directed spanning tree,and also considers the effects of disturbances and leader of non-zero control inputs in the system.Firstly,a novel distributed control protocol is designed for uncertain disturbances and leader of non-zero control inputs in MASs.Secondly,a novel dynamic event-triggered control(DETC)strategy is proposed,which eliminates the need for continuous communication between agents and reduces communication resources between agents.By introducing dynamic thresholds,the complexity of excluding Zeno behavior within the system is reduced.Finally,the effectiveness of the proposed theory is validated through numerical simulation.展开更多
Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metavers...Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.展开更多
This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities.To eli...This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities.To eliminate nonlinearities,neural networks are applied to approximate the inherent dynamics of the system.In addition,due to the limitations of the actual working conditions,each follower agent can only obtain the locally measurable partial state information of the leader agent.To address this problem,a neural network state observer based on the leader state information is designed.Then,a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region,which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time.Finally,a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.展开更多
The strategy evolution process of game players is highly uncertain due to random emergent situations and other external disturbances.This paper investigates the issue of strategy interaction and behavioral decision-ma...The strategy evolution process of game players is highly uncertain due to random emergent situations and other external disturbances.This paper investigates the issue of strategy interaction and behavioral decision-making among game players in simulated confrontation scenarios within a random interference environment.It considers the possible risks that random disturbances may pose to the autonomous decision-making of game players,as well as the impact of participants’manipulative behaviors on the state changes of the players.A nonlinear mathematical model is established to describe the strategy decision-making process of the participants in this scenario.Subsequently,the strategy selection interaction relationship,strategy evolution stability,and dynamic decision-making process of the game players are investigated and verified by simulation experiments.The results show that maneuver-related parameters and random environmental interference factors have different effects on the selection and evolutionary speed of the agent’s strategies.Especially in a highly uncertain environment,even small information asymmetry or miscalculation may have a significant impact on decision-making.This also confirms the feasibility and effectiveness of the method proposed in the paper,which can better explain the behavioral decision-making process of the agent in the interaction process.This study provides feasibility analysis ideas and theoretical references for improving multi-agent interactive decision-making and the interpretability of the game system model.展开更多
Efficient exploration in complex coordination tasks has been considered a challenging problem in multi-agent reinforcement learning(MARL). It is significantly more difficult for those tasks with latent variables that ...Efficient exploration in complex coordination tasks has been considered a challenging problem in multi-agent reinforcement learning(MARL). It is significantly more difficult for those tasks with latent variables that agents cannot directly observe. However, most of the existing latent variable discovery methods lack a clear representation of latent variables and an effective evaluation of the influence of latent variables on the agent. In this paper, we propose a new MARL algorithm based on the soft actor-critic method for complex continuous control tasks with confounders. It is called the multi-agent soft actor-critic with latent variable(MASAC-LV) algorithm, which uses variational inference theory to infer the compact latent variables representation space from a large amount of offline experience.Besides, we derive the counterfactual policy whose input has no latent variables and quantify the difference between the actual policy and the counterfactual policy via a distance function. This quantified difference is considered an intrinsic motivation that gives additional rewards based on how much the latent variable affects each agent. The proposed algorithm is evaluated on two collaboration tasks with confounders, and the experimental results demonstrate the effectiveness of MASAC-LV compared to other baseline algorithms.展开更多
Large-scale indoor 3D reconstruction with multiple robots faces challenges in core enabling technologies.This work contributes to a framework addressing localization,coordination,and vision processing for multi-agent ...Large-scale indoor 3D reconstruction with multiple robots faces challenges in core enabling technologies.This work contributes to a framework addressing localization,coordination,and vision processing for multi-agent reconstruction.A system architecture fusing visible light positioning,multi-agent path finding via reinforcement learning,and 360°camera techniques for 3D reconstruction is proposed.Our visible light positioning algorithm leverages existing lighting for centimeter-level localization without additional infrastructure.Meanwhile,a decentralized reinforcement learning approach is developed to solve the multi-agent path finding problem,with communications among agents optimized.Our 3D reconstruction pipeline utilizes equirectangular projection from 360°cameras to facilitate depth-independent reconstruction from posed monocular images using neural networks.Experimental validation demonstrates centimeter-level indoor navigation and 3D scene reconstruction capabilities of our framework.The challenges and limitations stemming from the above enabling technologies are discussed at the end of each corresponding section.In summary,this research advances fundamental techniques for multi-robot indoor 3D modeling,contributing to automated,data-driven applications through coordinated robot navigation,perception,and modeling.展开更多
基金supported in part by the National Natural Science Foundation of China(U1804147,61833001,61873139,61573129)the Innovative Scientists and Technicians Team of Henan Polytechnic University(T2019-2)the Innovative Scientists and Technicians Team of Henan Provincial High Education(20IRTSTHN019)。
文摘In this paper,an asymmetric bipartite consensus problem for the nonlinear multi-agent systems with cooperative and antagonistic interactions is studied under the event-triggered mechanism.For the agents described by a structurally balanced signed digraph,the asymmetric bipartite consensus objective is firstly defined,assigning the agents'output to different signs and module values.Considering with the completely unknown dynamics of the agents,a novel event-triggered model-free adaptive bipartite control protocol is designed based on the agents'triggered outputs and an equivalent compact form data model.By utilizing the Lyapunov analysis method,the threshold of the triggering condition is obtained.Subsequently,the asymptotic convergence of the tracking error is deduced and a sufficient condition is obtained based on the contraction mapping principle.Finally,the simulation example further demonstrates the effectiveness of the protocol.
基金This work was partially supported by the National Natural Science Foundation of China(62033003,62003098)the Local Innovative and Research Teams Project of Guangdong Special Support Program(2019BT02X353)the China Postdoctoral Science Foundation(2019M662813,2020T130124).
文摘This paper considers the human-in-the-loop leader-following consensus control problem of multi-agent systems(MASs)with unknown matched nonlinear functions and actuator faults.It is assumed that a human operator controls the MASs via sending the command signal to a non-autonomous leader which generates the desired trajectory.Moreover,the leader’s input is nonzero and not available to all followers.By using neural networks and fault estimators to approximate unknown nonlinear dynamics and identify the actuator faults,respectively,the neighborhood observer-based neural fault-tolerant controller with dynamic coupling gains is designed.It is proved that the state of each follower can synchronize with the leader’s state under a directed graph and all signals in the closed-loop system are guaranteed to be cooperatively uniformly ultimately bounded.Finally,simulation results are presented for verifying the effectiveness of the proposed control method.
基金supported by the National Natural Science Foundation of China(61773056)the Scientific and Technological Innovation Foundation of Shunde Graduate School,University of Science and Technology Beijing(USTB)(BK19AE018)+2 种基金the Fundamental Research Funds for the Central Universities of USTB(FRF-TP-20-09B,230201606500061,FRF-DF-20-35,FRF-BD-19-002A)supported by Zhejiang Natural Science Foundation(LD21F030001)supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(Ministry of Science and Information and Communications Technology)(NRF-2020R1A2C1005449)。
文摘This paper investigates the event-triggered security consensus problem for nonlinear multi-agent systems(MASs)under denial-of-service(Do S)attacks over an undirected graph.A novel adaptive memory observer-based anti-disturbance control scheme is presented to improve the observer accuracy by adding a buffer for the system output measurements.Meanwhile,this control scheme can also provide more reasonable control signals when Do S attacks occur.To save network resources,an adaptive memory event-triggered mechanism(AMETM)is also proposed and Zeno behavior is excluded.It is worth mentioning that the AMETM's updates do not require global information.Then,the observer and controller gains are obtained by using the linear matrix inequality(LMI)technique.Finally,simulation examples show the effectiveness of the proposed control scheme.
基金supported by the National Natural Science Foundation of China(6137510561403334)
文摘This paper investigates the consensus problem of second-order nonlinear multi-agent systems (MASs) via the sliding mode control (SMC) approach. The velocity of each agent is assumed to be unmeasurable. A second-order sliding mode observer is designed to estimate the velocity. Then a distributed discontinuous control law based on first-order SMC is presented to solve the consensus problem. Moreover, to overcome the chatting problem, two controllers based on the boundary layer method and the super-twisting algorithm respectively are presented. It is shown that the MASs will achieve consensus under some given conditions. Some examples are provided to demonstrate the effectiveness of the proposed control laws.
基金the National Natural Science Foundation of China(61673101,61973131,61733006,U1813201)the Science and Technology Project of Jilin Province(20210509053RQ)the Fourteenth Five Year Science Research Plan of Jilin Province(JJKH20220115KJ)。
文摘For a class of high-order nonlinear multi-agent systems with input hysteresis,an adaptive consensus output-feedback quantized control scheme with full state constraints is investigated.The major properties of the proposed control scheme are:1)According to the different hysteresis input characteristics of each agent in the multi-agent system,a hysteresis quantization inverse compensator is designed to eliminate the influence of hysteresis characteristics on the system while ensuring that the quantized signal maintains the desired value.2)A barrier Lyapunov function is introduced for the first time in the hysteretic multi-agent system.By constructing state constraint control strategy for the hysteretic multi-agent system,it ensures that all the states of the system are always maintained within a predetermined range.3)The designed adaptive consensus output-feedback quantization control scheme allows the hysteretic system to have unknown parameters and unknown disturbance,and ensures that the input signal transmitted between agents is the quantization value,and the introduced quantizer is implemented under the condition that only its sector bound property is required.The stability analysis has proved that all signals of the closed-loop are semi-globally uniformly bounded.The Star Sim hardware-in-the-loop simulation certificates the effectiveness of the proposed adaptive quantized control scheme.
基金supported in part by the National Natural Science Foundation of China(61873056,61621004,61420106016)the Fundamental Research Funds for the Central Universities in China(N2004001,N2004002,N182608004)the Research Fund of State Key Laboratory of Synthetical Automation for Process Industries in China(2013ZCX01)。
文摘This paper investigates the distributed fault-tolerant containment control(FTCC)problem of nonlinear multi-agent systems(MASs)under a directed network topology.The proposed control framework which is independent on the global information about the communication topology consists of two layers.Different from most existing distributed fault-tolerant control(FTC)protocols where the fault in one agent may propagate over network,the developed control method can eliminate the phenomenon of fault propagation.Based on the hierarchical control strategy,the FTCC problem with a directed graph can be simplified to the distributed containment control of the upper layer and the fault-tolerant tracking control of the lower layer.Finally,simulation results are given to demonstrate the effectiveness of the proposed control protocol.
基金Supported by the National Natural Science Foundation of China(No 71073071)the Social Science Foundation of Education Ministry(No 09YJA790088)the Major Program of Social Science Foundation of Jiangsu Education Office(No 2010-2-10).
文摘We investigate the third-order leader-following consensus problem of nonlinear multi-agent systems in undirected network topologies.Based on graph theory and Lyapunov stability theory,the adaptive control method is employed to achieve leader-following consensus in an undirected network of agents with nonlinear third-order dynamics against the perturbations.Simulation examples validate the correctness of the results and show that the control gains have a great influence on the convergence performance of errors for a short time.
基金National Key R&D Program of China(2018YFA0702200)National Natural Science Foundation of China(61627809,62173080)Liaoning Revitalization Talents Program(XLYC1801005)。
文摘This paper investigates adaptive containment control for a class of fractional-order multi-agent systems(FOMASs)with time-varying parameters and disturbances.By using the bounded estimation method,the difficulty generated by the timevarying parameters and disturbances is overcome.The command filter is introduced to solve the complexity problem inherent in adaptive backstepping control.Meanwhile,in order to eliminate the effect of filter errors,a novel distributed error compensating scheme is constructed,in which only the local information from the neighbor agents is utilized.Then,a distributed adaptive containment control scheme for FOMASs is developed based on backstepping to guarantee that the outputs of all the followers are steered to the convex hull spanned by the leaders.Based on the extension of Barbalat's lemma to fractional-order integrals,it can be proven that the containment errors and the compensating signals have asymptotic convergence.Finally,three simulation examples are given to show the feasibility and effectiveness of the proposed control method.
基金supported by the Science&Technology Department of Sichuan Province under Grant No.2020YJ0044。
文摘In this paper,the problems of robust consensus tracking control for the second-order multi-agent system with uncertain model parameters and nonlinear disturbances are considered.An adaptive control strategy is proposed to smooth the agent’s trajectory,and the neural network is constructed to estimate the system’s unknown components.The consensus conditions are demonstrated for tracking a leader with nonlinear dynamics under an adaptive control algorithm in the absence of model uncertainties.Then,the results are extended to the system with unknown time-varying disturbances by applying the neural network estimation to compensating for the uncertain parts of the agents’models.Update laws are designed based on the Lyapunov function terms to ensure the effectiveness of robust control.Finally,the theoretical results are verified by numerical simulations,and a comparative experiment is conducted,showing that the trajectories generated by the proposed method exhibit less oscillation and converge faster.
基金This work was supported by the National Key R&D Program of China(Grant No.2022YFB3305600)the National Natural Science Foundation of China(Grant Nos.62103015,62141604 and 92067204)the Fundamental Research Funds for Central Universities of China(Grant No.YWF-23-03-QB-019).
文摘This paper investigates the consensus-based formation control problem for multi-agent systems with unknown nonlinear dynamics.To achieve the desired formation,we propose two formation controllers to achieve the desired formation,one based on system states and the other on system outputs.The proposed controllers utilize adaptive gains to avoid global information and neural networks to estimate and compensate for nonlinearities.The proposed event-triggered schemes avoid continuous communication among agents and exclude the Zeno behavior.Stability analysis reveals that formation errors are bounded,and numerical simulations are used to validate the effectiveness of the proposed approaches.
基金National Natural Science Foundation of China(No.61963029)Jiangxi Provincial Natural Science Foundation(Nos.20224BAB202027 and 20232ACB202007)。
文摘To solve the synchronization and tracking problems,a cooperative control scheme is proposed for a class of higher-order multi-input and multi-output(MIMO)nonlinear multi-agent systems(MASs)subjected to uncertainties and external disturbances.First,coupled relationships among Laplace matrix,leader-following adjacency matrix and consensus error are analyzed based on undirected graph.Furthermore,nonlinear disturbance observers(NDOs)are designed to estimate compounded disturbances in MASs,and a distributed cooperative anti-disturbance control protocol is proposed for high-order MIMO nonlinear MASs based on the outputs of NDOs and dynamic surface control approach.Finally,the feasibility and effectiveness of the proposed scheme are proven based on Lyapunov stability theory and simulation experiments.
基金National Natural Science Foundation of China(No.62073296)Natural Science Foundation of Zhejiang Province,China(No.LZ23F030010)Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province,China Jiliang University(No.ZNZZSZ-CJLU2022-03)Rights and permissions。
文摘To address fixed-time consensus problems of a class of leader-follower second-order nonlinear multi-agent systems with uncertain external disturbances,the event-triggered fixed-time consensus protocol is proposed.First,the virtual velocity is designed based on the backstepping control method to achieve the system consensus and the bound on convergence time only depending on the system parameters.Second,an event-triggered mechanism is presented to solve the problem of frequent communication between agents,and triggered condition based on state information is given for each follower.It is available to save communication resources,and the Zeno behaviors are excluded.Then,the delay and switching topologies of the system are also discussed.Next,the system stabilization is analyzed by Lyapunov stability theory.Finally,simulation results demonstrate the validity of the presented method.
基金supported by the National Natural Science Foundation of China(62173175)。
文摘This paper studies the dynamic event-triggered leader-follower consensus of nonlinear multi-agent systems(MASs)under directed weighted graph containing a directed spanning tree,and also considers the effects of disturbances and leader of non-zero control inputs in the system.Firstly,a novel distributed control protocol is designed for uncertain disturbances and leader of non-zero control inputs in MASs.Secondly,a novel dynamic event-triggered control(DETC)strategy is proposed,which eliminates the need for continuous communication between agents and reduces communication resources between agents.By introducing dynamic thresholds,the complexity of excluding Zeno behavior within the system is reduced.Finally,the effectiveness of the proposed theory is validated through numerical simulation.
基金supported in part by NSFC (62102099, U22A2054, 62101594)in part by the Pearl River Talent Recruitment Program (2021QN02S643)+9 种基金Guangzhou Basic Research Program (2023A04J1699)in part by the National Research Foundation, SingaporeInfocomm Media Development Authority under its Future Communications Research Development ProgrammeDSO National Laboratories under the AI Singapore Programme under AISG Award No AISG2-RP-2020-019Energy Research Test-Bed and Industry Partnership Funding Initiative, Energy Grid (EG) 2.0 programmeDesCartes and the Campus for Research Excellence and Technological Enterprise (CREATE) programmeMOE Tier 1 under Grant RG87/22in part by the Singapore University of Technology and Design (SUTD) (SRG-ISTD-2021- 165)in part by the SUTD-ZJU IDEA Grant SUTD-ZJU (VP) 202102in part by the Ministry of Education, Singapore, through its SUTD Kickstarter Initiative (SKI 20210204)。
文摘Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.
基金the National Natural Science Foundation of China(62203356)Fundamental Research Funds for the Central Universities of China(31020210502002)。
文摘This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities.To eliminate nonlinearities,neural networks are applied to approximate the inherent dynamics of the system.In addition,due to the limitations of the actual working conditions,each follower agent can only obtain the locally measurable partial state information of the leader agent.To address this problem,a neural network state observer based on the leader state information is designed.Then,a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region,which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time.Finally,a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.
文摘The strategy evolution process of game players is highly uncertain due to random emergent situations and other external disturbances.This paper investigates the issue of strategy interaction and behavioral decision-making among game players in simulated confrontation scenarios within a random interference environment.It considers the possible risks that random disturbances may pose to the autonomous decision-making of game players,as well as the impact of participants’manipulative behaviors on the state changes of the players.A nonlinear mathematical model is established to describe the strategy decision-making process of the participants in this scenario.Subsequently,the strategy selection interaction relationship,strategy evolution stability,and dynamic decision-making process of the game players are investigated and verified by simulation experiments.The results show that maneuver-related parameters and random environmental interference factors have different effects on the selection and evolutionary speed of the agent’s strategies.Especially in a highly uncertain environment,even small information asymmetry or miscalculation may have a significant impact on decision-making.This also confirms the feasibility and effectiveness of the method proposed in the paper,which can better explain the behavioral decision-making process of the agent in the interaction process.This study provides feasibility analysis ideas and theoretical references for improving multi-agent interactive decision-making and the interpretability of the game system model.
基金supported in part by the National Natural Science Foundation of China (62136008,62236002,61921004,62173251,62103104)the “Zhishan” Scholars Programs of Southeast Universitythe Fundamental Research Funds for the Central Universities (2242023K30034)。
文摘Efficient exploration in complex coordination tasks has been considered a challenging problem in multi-agent reinforcement learning(MARL). It is significantly more difficult for those tasks with latent variables that agents cannot directly observe. However, most of the existing latent variable discovery methods lack a clear representation of latent variables and an effective evaluation of the influence of latent variables on the agent. In this paper, we propose a new MARL algorithm based on the soft actor-critic method for complex continuous control tasks with confounders. It is called the multi-agent soft actor-critic with latent variable(MASAC-LV) algorithm, which uses variational inference theory to infer the compact latent variables representation space from a large amount of offline experience.Besides, we derive the counterfactual policy whose input has no latent variables and quantify the difference between the actual policy and the counterfactual policy via a distance function. This quantified difference is considered an intrinsic motivation that gives additional rewards based on how much the latent variable affects each agent. The proposed algorithm is evaluated on two collaboration tasks with confounders, and the experimental results demonstrate the effectiveness of MASAC-LV compared to other baseline algorithms.
基金supported by Bright Dream Robotics and the HKUSTBDR Joint Research Institute Funding Scheme under Project HBJRI-FTP-005(Automated 3D Reconstruction using Robot-mounted 360-Degree Camera with Visible Light Positioning Technology for Building Information Modelling Applications,OKT22EG06).
文摘Large-scale indoor 3D reconstruction with multiple robots faces challenges in core enabling technologies.This work contributes to a framework addressing localization,coordination,and vision processing for multi-agent reconstruction.A system architecture fusing visible light positioning,multi-agent path finding via reinforcement learning,and 360°camera techniques for 3D reconstruction is proposed.Our visible light positioning algorithm leverages existing lighting for centimeter-level localization without additional infrastructure.Meanwhile,a decentralized reinforcement learning approach is developed to solve the multi-agent path finding problem,with communications among agents optimized.Our 3D reconstruction pipeline utilizes equirectangular projection from 360°cameras to facilitate depth-independent reconstruction from posed monocular images using neural networks.Experimental validation demonstrates centimeter-level indoor navigation and 3D scene reconstruction capabilities of our framework.The challenges and limitations stemming from the above enabling technologies are discussed at the end of each corresponding section.In summary,this research advances fundamental techniques for multi-robot indoor 3D modeling,contributing to automated,data-driven applications through coordinated robot navigation,perception,and modeling.
