This paper presents a novel optimal synchronization control method for multi-agent systems with input saturation.The multi-agent game theory is introduced to transform the optimal synchronization control problem into ...This paper presents a novel optimal synchronization control method for multi-agent systems with input saturation.The multi-agent game theory is introduced to transform the optimal synchronization control problem into a multi-agent nonzero-sum game.Then,the Nash equilibrium can be achieved by solving the coupled Hamilton–Jacobi–Bellman(HJB)equations with nonquadratic input energy terms.A novel off-policy reinforcement learning method is presented to obtain the Nash equilibrium solution without the system models,and the critic neural networks(NNs)and actor NNs are introduced to implement the presented method.Theoretical analysis is provided,which shows that the iterative control laws converge to the Nash equilibrium.Simulation results show the good performance of the presented method.展开更多
Based on the framework of BSP, a Hierarchical Bulk Synchronous Parallel (HBSP) performance model is introduced in this paper to capture the per formance optimization problem for various stages in parallel program deve...Based on the framework of BSP, a Hierarchical Bulk Synchronous Parallel (HBSP) performance model is introduced in this paper to capture the per formance optimization problem for various stages in parallel program development and to accurately predict the performance of a parallel program by considering fac tors causing variance at local computation and global communication. The related methodology has been applied to several real applications and the results show that HBSP is a suitable model for optimizing parallel programs.展开更多
基金Project supported by the National Key R&D Program of China(No.2018YFB1702300)the National Natural Science Foundation of China(Nos.61722312 and 61533017)。
文摘This paper presents a novel optimal synchronization control method for multi-agent systems with input saturation.The multi-agent game theory is introduced to transform the optimal synchronization control problem into a multi-agent nonzero-sum game.Then,the Nash equilibrium can be achieved by solving the coupled Hamilton–Jacobi–Bellman(HJB)equations with nonquadratic input energy terms.A novel off-policy reinforcement learning method is presented to obtain the Nash equilibrium solution without the system models,and the critic neural networks(NNs)and actor NNs are introduced to implement the presented method.Theoretical analysis is provided,which shows that the iterative control laws converge to the Nash equilibrium.Simulation results show the good performance of the presented method.
文摘Based on the framework of BSP, a Hierarchical Bulk Synchronous Parallel (HBSP) performance model is introduced in this paper to capture the per formance optimization problem for various stages in parallel program development and to accurately predict the performance of a parallel program by considering fac tors causing variance at local computation and global communication. The related methodology has been applied to several real applications and the results show that HBSP is a suitable model for optimizing parallel programs.
