位置：成果数据库 > 会议 > 会议详情页
Generalized Hamilton-Jacobi-Isaacs formulation-based neural network H-infinity control for constrain
  • 所属机构名称：中国科学院自动化研究所
  • 会议名称：Proceedings of 19th International Conference on Neural Information Processing
  • 成果类型：会议
  • 相关项目：基于自适应动态规划的非线性系统零和微分对策
作者： Yuzhu Huang|Derong Liu|Qinglai Wei|
同会议论文项目
基于自适应动态规划的非线性系统零和微分对策
期刊论文 15 会议论文 21 专利 2
同项目会议论文
An iterative ADP approach for solving a class of nonlinear zero-sum differential games
Optimal control for discrete-time nonlinear systems with unfixed initial state using adaptive dynami
Adaptive dynamic programming for finite-horizon optimal tracking control of a class of nonlinear sys
Adaptive dynamic programming with stable value iteration algorithm for discrete-time nonlinear syste
Heuristic dynamic programming-based H-infinity control of discrete-time affine nonlinear systems
Temperature control in water-gas shift reaction with adaptive dynamic programming
Nonlinear multi-person zero-sum differential games using iterative adaptive dynamic programming
Finite-horizon neural optimal tracking control for a class of nonlinear systems with unknown dynamic
Optimal battery management with ADHDP in smart home environments
Optimal control of unknown discrete-time nonlinear systems with constrained inputs using GDHP techni
Neural-network-based optimal control for a class of nonlinear discrete-time systems with control con
Optimal control for a class of unknown nonlinear systems via the iterative GDHP algorithm
Adaptive dynamic programming for optimal control of unknown nonlinear discrete-time systems
H-infinity control of unknown discrete-time nonlinear systems with control constraints using adaptiv
Adaptive optimal control for a class of nonlinear partially uncertain dynamic systems via policy ite
Finite horizon optimal tracking control for a class of discrete-time nonlinear systems
Adaptive dynamic programming for a class of discrete-time non-affine nonlinear systems with time-del
Self-learning control schemes for two-person zero-sum differential games of continuous-time nonlinea
A new optimal control method for discrete-time nonlinear systems with approximation errors
Neural-network-based optimal control for discrete-time nonlinear systems using general value iterati