Dear Editor,Underactuated autonomous surface vessels(ASVs)are increasingly attracting attention from researchers because of a wide range of applications[1].Consequently,path following,a typical functionality for ASVs,...Dear Editor,Underactuated autonomous surface vessels(ASVs)are increasingly attracting attention from researchers because of a wide range of applications[1].Consequently,path following,a typical functionality for ASVs,has become a research focus[2].Despite the abundant study results,some challenging issues are still worthy of exploration and resolution,two of which are addressed in this letter.The first one is related to the guidance law.Currently,common guidance methods in the ASV field include the line-of-sight(LOS)guidance[3]and vector field(VF)guidance[4].The response quality of LOS guidance is highly related to the lookahead distance;a constant lookahead distance may result in undesired phenomena such as the singularity problem and the reduction of trajectory smoothness of ASVs(see[5]).To this end,several works have proposed modified LOS guidance laws(see[6]).Although the above modifications,as pointed out by[7],the VF guidance exhibits smaller crosstrack errors and better performances than the LOS guidance.However,the existing VF guidance is only available for straight lines and orbits rather than curved paths,a considerable obstacle that limits its practical application(see[8],[9]).Thus,the VF guidance for curved path following deserves more in-depth study.展开更多
This paper addresses the parallel control of autonomous surface vehicles subject to external disturbances,state constraints,and input constraints in complex ocean environments with multiple obstacles.A safety-certifie...This paper addresses the parallel control of autonomous surface vehicles subject to external disturbances,state constraints,and input constraints in complex ocean environments with multiple obstacles.A safety-certified parallel model predictive control scheme with collision-avoiding capability is proposed for autonomous surface vehicles in the framework of parallel control.Specifically,an extended state observer is designed by leveraging historical and real-time data for concurrent learning to map the motion of autonomous surface vehicles from its physical system to its artificial counterpart.A parallel model predictive control law is developed on the basis of the artificial system for both physical and artificial autonomous surface vehicles to realize virtual-physical tracking control of vehicles subject to state and input constraints.To ensure safety,highorder discrete control barrier functions are encoded in the parallel model predictive control law as safety constraints such that collision avoidance with obstacles can be achieved.A recedinghorizon constrained optimization problem is constructed with the safety constraints encoded by control barrier functions for parallel model predictive control of autonomous surface vehicles and solved via neurodynamic optimization with projection neural networks.The effectiveness and characteristics of the proposed method are demonstrated via simulations for the safe trajectory tracking and automatic berthing of autonomous surface vehicles.展开更多
本文运用非对称随机波动(Asymmetric Stochastic Volatility,ASV)模型,以1997年为分界线,对我国沪深两市A股市场的市场波动反应模式进行了研究。实证结果表明:我国股票市场对外界信息的反应模式不仅存在非对称性,而且这种非对称反应特...本文运用非对称随机波动(Asymmetric Stochastic Volatility,ASV)模型,以1997年为分界线,对我国沪深两市A股市场的市场波动反应模式进行了研究。实证结果表明:我国股票市场对外界信息的反应模式不仅存在非对称性,而且这种非对称反应特征还具有阶段性:即在股市发展前期(1993～1996)市场波动反应具有非对称反转效应(Reversions in the Asymmetric Behavior of the Volatility);在股市发展后期(1997～2006)市场波动反应具有杠杆效应(Leverage Effect)。并且非对称反应强度随着时间的推移逐渐减小。展开更多
This paper is concerned with the cooperative target tracking of multiple autonomous surface vehicles(ASVs)under switching interaction topologies.For the target to be tracked,only its position can be measured/received ...This paper is concerned with the cooperative target tracking of multiple autonomous surface vehicles(ASVs)under switching interaction topologies.For the target to be tracked,only its position can be measured/received by some of the ASVs,and its velocity is unavailable to all the ASVs.A distributed extended state observer taking into consideration switching topologies is designed to integrally estimate unknown target dynamics and neighboring ASVs'dynamics.Accordingly,a novel kinematic controller is designed,which takes full advantage of known information and avoids the approximation of some virtual control vectors.Moreover,a disturbance observer is presented to estimate unknown time-varying environmental disturbance.Furthermore,a distributed dynamic controller is designed to regulate the involved ASVs to cooperatively track the target.It enables each ASV to adjust its forces and moments according to the received information from its neighbors.The effectiveness of the derived results is demonstrated through cooperative target tracking performance analysis for a tracking system composed of five interacting ASVs.展开更多
基金supported by the National Natural Science Foundation of China(62473243,62421004)the Fundamental Research Funds for the Provincial Universities(3072024 GH0404)+1 种基金the Key Research and Development Projects in Hainan Province(ZDYF2024GXJS009)the“Spring Wild Goose”Plan Project of Heilongjiang Province(CYQN24071).
文摘Dear Editor,Underactuated autonomous surface vessels(ASVs)are increasingly attracting attention from researchers because of a wide range of applications[1].Consequently,path following,a typical functionality for ASVs,has become a research focus[2].Despite the abundant study results,some challenging issues are still worthy of exploration and resolution,two of which are addressed in this letter.The first one is related to the guidance law.Currently,common guidance methods in the ASV field include the line-of-sight(LOS)guidance[3]and vector field(VF)guidance[4].The response quality of LOS guidance is highly related to the lookahead distance;a constant lookahead distance may result in undesired phenomena such as the singularity problem and the reduction of trajectory smoothness of ASVs(see[5]).To this end,several works have proposed modified LOS guidance laws(see[6]).Although the above modifications,as pointed out by[7],the VF guidance exhibits smaller crosstrack errors and better performances than the LOS guidance.However,the existing VF guidance is only available for straight lines and orbits rather than curved paths,a considerable obstacle that limits its practical application(see[8],[9]).Thus,the VF guidance for curved path following deserves more in-depth study.
基金supported in part by the National Science and Technology Major Project(2022ZD0119902)the National Natural Science Foundation of China(52471372,623B2018,62203015,62233001)+4 种基金the Liaoning Revitalization Leading Talents Program(XLYC2402054)the Key Basic Research of Dalian(2023JJ11CG008)the Fundamental Research Funds for the Central Universities(3132023508)the Collaborative Research Fund of Hong Kong Research Grants Council(C1013-24G)the Cultivation Program for the Excellent Doctoral Dissertation of Dalian Maritime University(2023YBPY005).
文摘This paper addresses the parallel control of autonomous surface vehicles subject to external disturbances,state constraints,and input constraints in complex ocean environments with multiple obstacles.A safety-certified parallel model predictive control scheme with collision-avoiding capability is proposed for autonomous surface vehicles in the framework of parallel control.Specifically,an extended state observer is designed by leveraging historical and real-time data for concurrent learning to map the motion of autonomous surface vehicles from its physical system to its artificial counterpart.A parallel model predictive control law is developed on the basis of the artificial system for both physical and artificial autonomous surface vehicles to realize virtual-physical tracking control of vehicles subject to state and input constraints.To ensure safety,highorder discrete control barrier functions are encoded in the parallel model predictive control law as safety constraints such that collision avoidance with obstacles can be achieved.A recedinghorizon constrained optimization problem is constructed with the safety constraints encoded by control barrier functions for parallel model predictive control of autonomous surface vehicles and solved via neurodynamic optimization with projection neural networks.The effectiveness and characteristics of the proposed method are demonstrated via simulations for the safe trajectory tracking and automatic berthing of autonomous surface vehicles.
文摘本文运用非对称随机波动(Asymmetric Stochastic Volatility,ASV)模型,以1997年为分界线,对我国沪深两市A股市场的市场波动反应模式进行了研究。实证结果表明:我国股票市场对外界信息的反应模式不仅存在非对称性,而且这种非对称反应特征还具有阶段性:即在股市发展前期(1993～1996)市场波动反应具有非对称反转效应(Reversions in the Asymmetric Behavior of the Volatility);在股市发展后期(1997～2006)市场波动反应具有杠杆效应(Leverage Effect)。并且非对称反应强度随着时间的推移逐渐减小。
基金supported in part by the National Science Foundation of China(61873335,61833011)the Project of Scie nce and Technology Commission of Shanghai Municipality,China(20ZR1420200,21SQBS01600,19510750300,21190780300)。
文摘This paper is concerned with the cooperative target tracking of multiple autonomous surface vehicles(ASVs)under switching interaction topologies.For the target to be tracked,only its position can be measured/received by some of the ASVs,and its velocity is unavailable to all the ASVs.A distributed extended state observer taking into consideration switching topologies is designed to integrally estimate unknown target dynamics and neighboring ASVs'dynamics.Accordingly,a novel kinematic controller is designed,which takes full advantage of known information and avoids the approximation of some virtual control vectors.Moreover,a disturbance observer is presented to estimate unknown time-varying environmental disturbance.Furthermore,a distributed dynamic controller is designed to regulate the involved ASVs to cooperatively track the target.It enables each ASV to adjust its forces and moments according to the received information from its neighbors.The effectiveness of the derived results is demonstrated through cooperative target tracking performance analysis for a tracking system composed of five interacting ASVs.
