总部位于智利圣地亚哥的CMPC公司近日表示，该公司董事会已经通过对公司位于巴西RioGrande do Sul的Guaiba浆厂的扩产计划。“作为CMPC公司历史上最大的投资，将成为CMPC公司的一个里程碑。”CMPC公司首席执行官Hernan Rodriguez表示，...总部位于智利圣地亚哥的CMPC公司近日表示，该公司董事会已经通过对公司位于巴西RioGrande do Sul的Guaiba浆厂的扩产计划。“作为CMPC公司历史上最大的投资，将成为CMPC公司的一个里程碑。”CMPC公司首席执行官Hernan Rodriguez表示，“Guaiba浆厂二期工程将会使我们在全球纸浆市场的份额增加1倍，并有助于满足我们的客户对高质量木浆不断增长的需求。”展开更多
In complex water environments,search tasks often involve multiple Autonomous Underwater Vehicles(AUVs),and a single centralized control cannot handle the complexity and computational burden of large-scale systems.Targ...In complex water environments,search tasks often involve multiple Autonomous Underwater Vehicles(AUVs),and a single centralized control cannot handle the complexity and computational burden of large-scale systems.Target search in complex water environments has always been a major challenge in the field of underwater robots.To address this problem,this paper proposes a multi-biomimetic robot fish collaborative target search method based on Distributed Model Predictive Control(DMPC).First,we established a bionic robot fish kinematic model and a multi-biomimetic robot fish communication model;second,this paper proposed a distributed model predictive control algorithm based on the distributed search theory framework,so that the bionic robot fish can dynamically adjust their search path according to each other’s position information and search status,avoid repeated coverage or missing areas,and thus improve the search efficiency;third,we conducted simulation experiments based on DMPC,and the results showed that the proposed method has a target search success rate of more than 90%in static targets,dynamic targets,and obstacle environments.Finally,we compared this method with Centralized Model Predictive Control(CMPC)and Random Walk(RW)algorithms.The DMPC approach demonstrates significant advantages,achieving a remarkable target search success rate of 94.17%.These findings comprehensively validate the effectiveness and superiority of the proposed methodology.It can be seen that DMPC can effectively dispatch multiple bionic robot fish to work together to achieve efficient search of vast waters.It can significantly improve the flexibility,scalability,robustness and cooperation efficiency of the system and has broad application prospects.展开更多
分布式模型预测控制(Distributed model predictive control,DMPC)是一类用十多输入多输出的人规模系统的控制方式.每个智能体通过相互协作完成整个系统的摔制.已有的分布式预测摔制算法可以划分为迭代式算法和非迭代算法:迭代算法在迭...分布式模型预测控制(Distributed model predictive control,DMPC)是一类用十多输入多输出的人规模系统的控制方式.每个智能体通过相互协作完成整个系统的摔制.已有的分布式预测摔制算法可以划分为迭代式算法和非迭代算法:迭代算法在迭代到收敛情况下,具有集中式预测控制(Centralized model predictive control,CMPC)算法的性能,但迭代次数过多,子系统问通信量人;非迭代算法不需要迭代,但性能有一定损失.本文提出了一种基十串联结构的非迭代分布式预测控制算法.本文算法在串联结构系统中可以有效减少计算量,并结合氧化铝碳分解(Alumina continuous carbonationdecomposition process,ACCDP)这一串联过程,通过仿真验证了算法的有效性;同时分析了算法运用在串联结构下的性能并证明了其稳定性.展开更多
文摘总部位于智利圣地亚哥的CMPC公司近日表示，该公司董事会已经通过对公司位于巴西RioGrande do Sul的Guaiba浆厂的扩产计划。“作为CMPC公司历史上最大的投资，将成为CMPC公司的一个里程碑。”CMPC公司首席执行官Hernan Rodriguez表示，“Guaiba浆厂二期工程将会使我们在全球纸浆市场的份额增加1倍，并有助于满足我们的客户对高质量木浆不断增长的需求。”
基金funded by National Natural Science Foundation of China(Nos.62473236,62073196).
文摘In complex water environments,search tasks often involve multiple Autonomous Underwater Vehicles(AUVs),and a single centralized control cannot handle the complexity and computational burden of large-scale systems.Target search in complex water environments has always been a major challenge in the field of underwater robots.To address this problem,this paper proposes a multi-biomimetic robot fish collaborative target search method based on Distributed Model Predictive Control(DMPC).First,we established a bionic robot fish kinematic model and a multi-biomimetic robot fish communication model;second,this paper proposed a distributed model predictive control algorithm based on the distributed search theory framework,so that the bionic robot fish can dynamically adjust their search path according to each other’s position information and search status,avoid repeated coverage or missing areas,and thus improve the search efficiency;third,we conducted simulation experiments based on DMPC,and the results showed that the proposed method has a target search success rate of more than 90%in static targets,dynamic targets,and obstacle environments.Finally,we compared this method with Centralized Model Predictive Control(CMPC)and Random Walk(RW)algorithms.The DMPC approach demonstrates significant advantages,achieving a remarkable target search success rate of 94.17%.These findings comprehensively validate the effectiveness and superiority of the proposed methodology.It can be seen that DMPC can effectively dispatch multiple bionic robot fish to work together to achieve efficient search of vast waters.It can significantly improve the flexibility,scalability,robustness and cooperation efficiency of the system and has broad application prospects.
文摘分布式模型预测控制(Distributed model predictive control,DMPC)是一类用十多输入多输出的人规模系统的控制方式.每个智能体通过相互协作完成整个系统的摔制.已有的分布式预测摔制算法可以划分为迭代式算法和非迭代算法:迭代算法在迭代到收敛情况下,具有集中式预测控制(Centralized model predictive control,CMPC)算法的性能,但迭代次数过多,子系统问通信量人;非迭代算法不需要迭代,但性能有一定损失.本文提出了一种基十串联结构的非迭代分布式预测控制算法.本文算法在串联结构系统中可以有效减少计算量,并结合氧化铝碳分解(Alumina continuous carbonationdecomposition process,ACCDP)这一串联过程,通过仿真验证了算法的有效性;同时分析了算法运用在串联结构下的性能并证明了其稳定性.
