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
This paper considers the problem of estimating unknown sparse time-varying signals for stochastic dynamic systems.To deal with the challenges of extensive sparsity,we resort to the compressed sensing method and propos...This paper considers the problem of estimating unknown sparse time-varying signals for stochastic dynamic systems.To deal with the challenges of extensive sparsity,we resort to the compressed sensing method and propose a compressed Kalman filter(KF)algorithm.Our algorithm first compresses the original high-dimensional sparse regression vector via the sensing matrix and then obtains a KF estimate in the compressed low-dimensional space.Subsequently,the original high-dimensional sparse signals can be well recovered by a reconstruction technique.To ensure stability and establish upper bounds on the estimation errors,we introduce a compressed excitation condition without imposing independence or stationarity on the system signal,and therefore suitable for feedback systems.We further present the performance of the compressed KF algorithm.Specifically,we show that the mean square compressed tracking error matrix can be approximately calculated by a linear deterministic difference matrix equation,which can be readily evaluated,analyzed,and optimized.Finally,a numerical example demonstrates that our algorithm outperforms the standard uncompressed KF algorithm and other compressed algorithms for estimating high-dimensional sparse signals.展开更多
Swarming behaviors play an eminent role in both biological and engineering research, and show great potential applications in many emerging fields. Traditional swarming models still lack integrity, uniformity, and sta...Swarming behaviors play an eminent role in both biological and engineering research, and show great potential applications in many emerging fields. Traditional swarming models still lack integrity, uniformity, and stability in swarm forming processes,resulting in fragmentation and void phenomena. Inspired by the shepherding behaviors observed in nature, we propose an integrated negotiation-control scheme for distributed swarm control of massive robots. The core idea of this scheme is that the robots at the boundary of the group herd the internal robots to form an equilibrium swarm. For this purpose, we introduce a concept of virtual group center towards which boundary robots herd internal robots. Then, a distributed negotiation mechanism is designed to allow each robot to negotiate the virtual group center only through local interactions with its neighbors. After that, we propose a shepherding-inspired swarm control law to drive a group of robots to form an integrated, uniform, and stable configuration from any initial states. Both numerical and flight simulations are presented to verify the effectiveness of our proposed swarm control scheme.展开更多
Initialization speed is one of the most important factors in network real time kinematic(NRTK)performance.Owing to the low correlation among the error sources of reference stations,it is difficult to fix reference sta...Initialization speed is one of the most important factors in network real time kinematic(NRTK)performance.Owing to the low correlation among the error sources of reference stations,it is difficult to fix reference station ambiguities of long-range NRTK quickly.In traditional reference stations ambiguity resolution(AR)methods,baselines are usually solved independently which is called baseline solution(BS)mode in this study.Because the correlations among baselines are not taken into consideration in ambiguities estimation,the AR speed is slow.Generally,tens of minutes or longer time is required to initialize.We propose a network solution(NS)mode approach,in which the correlations among the double-difference ambiguities(DDAs)as well as double-difference ionospheric delays(DDIDs)of different baselines are considered in estimating float ambiguity solutions.Experimental results show that the float ambiguity solutions obtained are more accurate with an improved consistency.Thus,initialization speed is significantly increased by 18%in NS mode.展开更多
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supported by the National Key Research and Development Program of China(Grant No.2022YFB3305600)the National Natural Science Foundation of China(Grant Nos.61621003,62141604)+1 种基金the China Postdoctoral Science Foundation(Grant No.2022M722926)the Major Key Project of Peng Cheng Laboratory(Grant No.PCL2023AS1-2)。
文摘This paper considers the problem of estimating unknown sparse time-varying signals for stochastic dynamic systems.To deal with the challenges of extensive sparsity,we resort to the compressed sensing method and propose a compressed Kalman filter(KF)algorithm.Our algorithm first compresses the original high-dimensional sparse regression vector via the sensing matrix and then obtains a KF estimate in the compressed low-dimensional space.Subsequently,the original high-dimensional sparse signals can be well recovered by a reconstruction technique.To ensure stability and establish upper bounds on the estimation errors,we introduce a compressed excitation condition without imposing independence or stationarity on the system signal,and therefore suitable for feedback systems.We further present the performance of the compressed KF algorithm.Specifically,we show that the mean square compressed tracking error matrix can be approximately calculated by a linear deterministic difference matrix equation,which can be readily evaluated,analyzed,and optimized.Finally,a numerical example demonstrates that our algorithm outperforms the standard uncompressed KF algorithm and other compressed algorithms for estimating high-dimensional sparse signals.
基金supported by the National Key R&D Program of China (Grant No. 2022YFB3305600)the National Natural Science Foundation of China (Grant Nos. 62103015 and 62141604)+1 种基金the China Postdoctoral Science Foundation (Grant No. 2023M740185)the Postdoctoral Fellows of Beihang “Zhuoyue” Program。
文摘Swarming behaviors play an eminent role in both biological and engineering research, and show great potential applications in many emerging fields. Traditional swarming models still lack integrity, uniformity, and stability in swarm forming processes,resulting in fragmentation and void phenomena. Inspired by the shepherding behaviors observed in nature, we propose an integrated negotiation-control scheme for distributed swarm control of massive robots. The core idea of this scheme is that the robots at the boundary of the group herd the internal robots to form an equilibrium swarm. For this purpose, we introduce a concept of virtual group center towards which boundary robots herd internal robots. Then, a distributed negotiation mechanism is designed to allow each robot to negotiate the virtual group center only through local interactions with its neighbors. After that, we propose a shepherding-inspired swarm control law to drive a group of robots to form an integrated, uniform, and stable configuration from any initial states. Both numerical and flight simulations are presented to verify the effectiveness of our proposed swarm control scheme.
基金supported in part by the National Key Research and Development Program of China(Grant No.2016YFB0800401)in part by the National Natural Science Foundation of China(Grant Nos.61621003,61532020&11472290).
文摘Initialization speed is one of the most important factors in network real time kinematic(NRTK)performance.Owing to the low correlation among the error sources of reference stations,it is difficult to fix reference station ambiguities of long-range NRTK quickly.In traditional reference stations ambiguity resolution(AR)methods,baselines are usually solved independently which is called baseline solution(BS)mode in this study.Because the correlations among baselines are not taken into consideration in ambiguities estimation,the AR speed is slow.Generally,tens of minutes or longer time is required to initialize.We propose a network solution(NS)mode approach,in which the correlations among the double-difference ambiguities(DDAs)as well as double-difference ionospheric delays(DDIDs)of different baselines are considered in estimating float ambiguity solutions.Experimental results show that the float ambiguity solutions obtained are more accurate with an improved consistency.Thus,initialization speed is significantly increased by 18%in NS mode.
