车间调度作为车间制造系统的重要组成部分,影响着整个车间制造系统的敏捷性和智能性.但是,由于资源和工艺约束的并存,使得车间调度成为一类NP-hard问题.基于静态的智能算法与动态的多Agent思想,提出了一种结合通用部分全局规划(generali...车间调度作为车间制造系统的重要组成部分,影响着整个车间制造系统的敏捷性和智能性.但是,由于资源和工艺约束的并存,使得车间调度成为一类NP-hard问题.基于静态的智能算法与动态的多Agent思想,提出了一种结合通用部分全局规划(generalized partial global planning,GPGP)机制与多种智能算法的多Agent车间调度模型,设计了从"初始宏观调度"到"微观再调度"的大规模复杂问题的调度步骤,并构建了一个柔性强且Agent可自我动态调度的仿真系统.同时,从理论上总结了GPGP基本协同机制的策略,实现了二级多目标优化调度.最后使用DECAF仿真Agent软件模拟了车间调度的GPGP协同机制,并与CNP,NONE机制进行了比较.结果表明,所提出的模型不仅提高了调度的效率,而且降低了资源的损耗.展开更多
Using the traditional swarm intelligence algorithm to solve the cooperative path planning problem for multi-UAVs is easy to incur the problems of local optimization and a slow convergence rate.A cooperative path plann...Using the traditional swarm intelligence algorithm to solve the cooperative path planning problem for multi-UAVs is easy to incur the problems of local optimization and a slow convergence rate.A cooperative path planning method for multi-UAVs based on the improved sheep optimization is proposed to tackle these.Firstly,based on the three-dimensional planning space,a multi-UAV cooperative cost function model is established according to the path planning requirements,and an initial track set is constructed by combining multiple-population ideas.Then an improved sheep optimization is proposed and used to solve the path planning problem and obtain multiple cooperative paths.The simulation results show that the sheep optimization can meet the requirements of path planning and realize the cooperative path planning of multi-UAVs.Compared with grey wolf optimizer(GWO),improved gray wolf optimizer(IGWO),chaotic gray wolf optimizer(CGWO),differential evolution(DE)algorithm,and particle swam optimization(PSO),the convergence speed and search accuracy of the improved sheep optimization are significantly improved.展开更多
This paper considers a leader-following tracking control problem for second-order multiagent systems(MASs) under measurement noises and directed communication channels.It is assumed that each follower-agent can measur...This paper considers a leader-following tracking control problem for second-order multiagent systems(MASs) under measurement noises and directed communication channels.It is assumed that each follower-agent can measure the relative positions and velocities of its neighbors in a noisy environment.Based on a novel velocity decomposition technique,a neighbor-based control law is designed to realize local control strategies for these continuous-time agents.It is shown that the proposed consensus protocol can guarantee that all the follower-agents track the active leader.In addition,this result is extended to a more general case with switching topologies.Finally,a numerical example is given for illustration.展开更多
文摘车间调度作为车间制造系统的重要组成部分,影响着整个车间制造系统的敏捷性和智能性.但是,由于资源和工艺约束的并存,使得车间调度成为一类NP-hard问题.基于静态的智能算法与动态的多Agent思想,提出了一种结合通用部分全局规划(generalized partial global planning,GPGP)机制与多种智能算法的多Agent车间调度模型,设计了从"初始宏观调度"到"微观再调度"的大规模复杂问题的调度步骤,并构建了一个柔性强且Agent可自我动态调度的仿真系统.同时,从理论上总结了GPGP基本协同机制的策略,实现了二级多目标优化调度.最后使用DECAF仿真Agent软件模拟了车间调度的GPGP协同机制,并与CNP,NONE机制进行了比较.结果表明,所提出的模型不仅提高了调度的效率,而且降低了资源的损耗.
基金supported in part by the Fundamental Research Funds for the Central Universities(No.NZ18008)。
文摘Using the traditional swarm intelligence algorithm to solve the cooperative path planning problem for multi-UAVs is easy to incur the problems of local optimization and a slow convergence rate.A cooperative path planning method for multi-UAVs based on the improved sheep optimization is proposed to tackle these.Firstly,based on the three-dimensional planning space,a multi-UAV cooperative cost function model is established according to the path planning requirements,and an initial track set is constructed by combining multiple-population ideas.Then an improved sheep optimization is proposed and used to solve the path planning problem and obtain multiple cooperative paths.The simulation results show that the sheep optimization can meet the requirements of path planning and realize the cooperative path planning of multi-UAVs.Compared with grey wolf optimizer(GWO),improved gray wolf optimizer(IGWO),chaotic gray wolf optimizer(CGWO),differential evolution(DE)algorithm,and particle swam optimization(PSO),the convergence speed and search accuracy of the improved sheep optimization are significantly improved.
基金supported by the National Natural Science Foundation of China under Grant No.61174070the Specialized Research Found for the Doctoral Program under Grant No.20110172110033
文摘This paper considers a leader-following tracking control problem for second-order multiagent systems(MASs) under measurement noises and directed communication channels.It is assumed that each follower-agent can measure the relative positions and velocities of its neighbors in a noisy environment.Based on a novel velocity decomposition technique,a neighbor-based control law is designed to realize local control strategies for these continuous-time agents.It is shown that the proposed consensus protocol can guarantee that all the follower-agents track the active leader.In addition,this result is extended to a more general case with switching topologies.Finally,a numerical example is given for illustration.
