本文针对随机复杂动态网络,给出了网络在均方意义下实现双跟踪的定义,提出了新的控制策略。本文考虑的复杂网络是由节点与链路共同组成,并且采用两个随机微分方程来对节点与链路的动态分别进行了建模。假设节点的状态信息是可以被利用的...本文针对随机复杂动态网络,给出了网络在均方意义下实现双跟踪的定义,提出了新的控制策略。本文考虑的复杂网络是由节点与链路共同组成,并且采用两个随机微分方程来对节点与链路的动态分别进行了建模。假设节点的状态信息是可以被利用的,链路的状态信息是不可以被利用的,基于此,在节点中设计了控制器,同时在链路中设计了耦合关系。通过这两个部分的共同作用可以使得节点跟踪上任意给定的跟踪目标,同时链路也可以跟踪上任意给定的跟踪目标。最后给出数值仿真验证了本文所提控制方案的有效性。In this paper, the definition of double tracking in the meaning of mean square for stochastic complex dynamic networks is given, and a new control strategy is proposed. Complex network considered in this paper is composed of nodes and links, and two stochastic differential equations are used to model the dynamics of nodes and links respectively. It is assumed that the state information of the nodes can be used, and the state information of the links can not be used. Based on this, the controller is designed in the node and the coupling relationship is designed in the link. Through the joint action of these two parts, the nodes can track any given tracking targets, and the links can also track any given tracking targets. Finally, numerical simulation is given to verify the effectiveness of the proposed control scheme.展开更多
复杂动态网络可以认为是由“节点子系统”和“连接边子系统”相互耦合而成的。本文首先采用两个向量微分方程来分别对两个子系统进行了建模,并且给出了网络实现二阶跟踪控制的定义。进一步给出了本文的控制目标,通过设计相应的二阶跟踪...复杂动态网络可以认为是由“节点子系统”和“连接边子系统”相互耦合而成的。本文首先采用两个向量微分方程来分别对两个子系统进行了建模,并且给出了网络实现二阶跟踪控制的定义。进一步给出了本文的控制目标,通过设计相应的二阶跟踪控制方案最终不仅可以使得网络中的节点实现位置跟踪,同时其也能按理想的速度来实现跟踪。最后,通过一个双连杆机器人机械臂的仿真实例对所提控制方案的有效性进行了验证。The complex dynamic network can be considered as being formed by the mutual coupling of “nodes subsystem” and “links subsystem”. In this paper, two vector differential equations are first used to model the two subsystems separately, and the definition of second-order tracking control for the network is provided. Furthermore, the control objective of this paper is presented. By designing an appropriate second-order tracking control scheme, the nodes in the network can achieve not only position tracking but also tracking at the desired speed. Finally, the effectiveness of the proposed control scheme is validated through a simulation example of a two-link robotic manipulator.展开更多
文摘本文针对随机复杂动态网络,给出了网络在均方意义下实现双跟踪的定义,提出了新的控制策略。本文考虑的复杂网络是由节点与链路共同组成,并且采用两个随机微分方程来对节点与链路的动态分别进行了建模。假设节点的状态信息是可以被利用的,链路的状态信息是不可以被利用的,基于此,在节点中设计了控制器,同时在链路中设计了耦合关系。通过这两个部分的共同作用可以使得节点跟踪上任意给定的跟踪目标,同时链路也可以跟踪上任意给定的跟踪目标。最后给出数值仿真验证了本文所提控制方案的有效性。In this paper, the definition of double tracking in the meaning of mean square for stochastic complex dynamic networks is given, and a new control strategy is proposed. Complex network considered in this paper is composed of nodes and links, and two stochastic differential equations are used to model the dynamics of nodes and links respectively. It is assumed that the state information of the nodes can be used, and the state information of the links can not be used. Based on this, the controller is designed in the node and the coupling relationship is designed in the link. Through the joint action of these two parts, the nodes can track any given tracking targets, and the links can also track any given tracking targets. Finally, numerical simulation is given to verify the effectiveness of the proposed control scheme.
文摘复杂动态网络可以认为是由“节点子系统”和“连接边子系统”相互耦合而成的。本文首先采用两个向量微分方程来分别对两个子系统进行了建模,并且给出了网络实现二阶跟踪控制的定义。进一步给出了本文的控制目标,通过设计相应的二阶跟踪控制方案最终不仅可以使得网络中的节点实现位置跟踪,同时其也能按理想的速度来实现跟踪。最后,通过一个双连杆机器人机械臂的仿真实例对所提控制方案的有效性进行了验证。The complex dynamic network can be considered as being formed by the mutual coupling of “nodes subsystem” and “links subsystem”. In this paper, two vector differential equations are first used to model the two subsystems separately, and the definition of second-order tracking control for the network is provided. Furthermore, the control objective of this paper is presented. By designing an appropriate second-order tracking control scheme, the nodes in the network can achieve not only position tracking but also tracking at the desired speed. Finally, the effectiveness of the proposed control scheme is validated through a simulation example of a two-link robotic manipulator.
