靶材刻蚀特性是研究磁控溅射靶材利用率、薄膜生长速度和薄膜质量的关键因素。本文用有限元分析软件ANSYS模拟了磁控溅射放电空间的磁场分布,用粒子模拟软件OOPIC Pro(object oriented particlein cell)模拟了放电过程,最后用SRIM(stopp...靶材刻蚀特性是研究磁控溅射靶材利用率、薄膜生长速度和薄膜质量的关键因素。本文用有限元分析软件ANSYS模拟了磁控溅射放电空间的磁场分布,用粒子模拟软件OOPIC Pro(object oriented particlein cell)模拟了放电过程,最后用SRIM(stopping and range of ions in matter)模拟了靶材的溅射特性,得到了靶材的刻蚀形貌和刻蚀速度,并讨论了不同工作气压和不同阴极电压对靶材刻蚀的影响。模拟结果表明:靶材刻蚀形貌与磁场分布有关,磁通密度越强,对应的靶材位置刻蚀越深;靶材的刻蚀速度随阴极电压的增大而增大,而当工作气压增大时,靶材的刻蚀速度先增大后趋向平衡,当工作气压超过一定的值时,刻蚀速度随气压的增大开始减小。模拟结果与实验观测进行了比较,二者符合较好。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
In many engineering networks, only a part of target state variables are required to be estimated.On the other hand,multi-layer complex network exists widely in practical situations.In this paper, the state estimation ...In many engineering networks, only a part of target state variables are required to be estimated.On the other hand,multi-layer complex network exists widely in practical situations.In this paper, the state estimation of target state variables in multi-layer complex dynamical networks with nonlinear node dynamics is studied.A suitable functional state observer is constructed with the limited measurement.The parameters of the designed functional observer are obtained from the algebraic method and the stability of the functional observer is proven by the Lyapunov theorem.Some necessary conditions that need to be satisfied for the design of the functional state observer are obtained.Different from previous studies, in the multi-layer complex dynamical network with nonlinear node dynamics, the proposed method can estimate the state of target variables on some layers directly instead of estimating all the individual states.Thus, it can greatly reduce the placement of observers and computational cost.Numerical simulations with the three-layer complex dynamical network composed of three-dimensional nonlinear dynamical nodes are developed to verify the effectiveness of the method.展开更多
This paper researches the adaptive scheduling problem of multiple electronic support measures(multi-ESM) in a ground moving radar targets tracking application. It is a sequential decision-making problem in uncertain e...This paper researches the adaptive scheduling problem of multiple electronic support measures(multi-ESM) in a ground moving radar targets tracking application. It is a sequential decision-making problem in uncertain environment. For adaptive selection of appropriate ESMs, we generalize an approximate dynamic programming(ADP) framework to the dynamic case. We define the environment model and agent model, respectively. To handle the partially observable challenge, we apply the unsented Kalman filter(UKF) algorithm for belief state estimation. To reduce the computational burden, a simulation-based approach rollout with a redesigned base policy is proposed to approximate the long-term cumulative reward. Meanwhile, Monte Carlo sampling is combined into the rollout to estimate the expectation of the rewards. The experiments indicate that our method outperforms other strategies due to its better performance in larger-scale problems.展开更多
文摘靶材刻蚀特性是研究磁控溅射靶材利用率、薄膜生长速度和薄膜质量的关键因素。本文用有限元分析软件ANSYS模拟了磁控溅射放电空间的磁场分布,用粒子模拟软件OOPIC Pro(object oriented particlein cell)模拟了放电过程,最后用SRIM(stopping and range of ions in matter)模拟了靶材的溅射特性,得到了靶材的刻蚀形貌和刻蚀速度,并讨论了不同工作气压和不同阴极电压对靶材刻蚀的影响。模拟结果表明:靶材刻蚀形貌与磁场分布有关,磁通密度越强,对应的靶材位置刻蚀越深;靶材的刻蚀速度随阴极电压的增大而增大,而当工作气压增大时,靶材的刻蚀速度先增大后趋向平衡,当工作气压超过一定的值时,刻蚀速度随气压的增大开始减小。模拟结果与实验观测进行了比较,二者符合较好。
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.
基金Project supported by the National Natural Science Foundation of China (Grant Nos.62373197 and 61873326)。
文摘In many engineering networks, only a part of target state variables are required to be estimated.On the other hand,multi-layer complex network exists widely in practical situations.In this paper, the state estimation of target state variables in multi-layer complex dynamical networks with nonlinear node dynamics is studied.A suitable functional state observer is constructed with the limited measurement.The parameters of the designed functional observer are obtained from the algebraic method and the stability of the functional observer is proven by the Lyapunov theorem.Some necessary conditions that need to be satisfied for the design of the functional state observer are obtained.Different from previous studies, in the multi-layer complex dynamical network with nonlinear node dynamics, the proposed method can estimate the state of target variables on some layers directly instead of estimating all the individual states.Thus, it can greatly reduce the placement of observers and computational cost.Numerical simulations with the three-layer complex dynamical network composed of three-dimensional nonlinear dynamical nodes are developed to verify the effectiveness of the method.
基金supported by the National Natural Science Foundation of China(6157328561305133)
文摘This paper researches the adaptive scheduling problem of multiple electronic support measures(multi-ESM) in a ground moving radar targets tracking application. It is a sequential decision-making problem in uncertain environment. For adaptive selection of appropriate ESMs, we generalize an approximate dynamic programming(ADP) framework to the dynamic case. We define the environment model and agent model, respectively. To handle the partially observable challenge, we apply the unsented Kalman filter(UKF) algorithm for belief state estimation. To reduce the computational burden, a simulation-based approach rollout with a redesigned base policy is proposed to approximate the long-term cumulative reward. Meanwhile, Monte Carlo sampling is combined into the rollout to estimate the expectation of the rewards. The experiments indicate that our method outperforms other strategies due to its better performance in larger-scale problems.
