State estimation for a class of nonlinear differential games using differential neural networks

Survey on spacecraft orbital pursuit-evasion differential games

“…同时, 随着计算智能的发展, 神经网络方法、协同进化 算法以及强化学习等数值求解方法也被引入微分对策 中, García和Murano [54] Anderson和Grazier [58] 将航天器运动方程在圆参考 轨道处线性化, 使得协态方程(9)可以被解析表达. 再 将最优机动表达式(10)在剩余捕获时间τ=0处泰勒展 开, 忽略三阶以上高阶项, 代入到状态方程式(2)中, 得 到了航天器平面追逃界栅的解析表达式 [58] .…”

Differential Neural Networks for Identification and Filtering in Nonlinear Dynamic Games

“…For example, in [18], differential neural networks are used for the identification of dynamical systems; references [19][20][21] design differential neural network observers for adaptive state estimation; the works of [22,23] propose neural network controllers for several applications; and [24] shows a compendium of differential neural networks for identification, state estimation, and control of nonlinear systems. Also, [25] treats the state estimation problem for affine nonlinear differential games using a differential neural network observer, and in [26], a nearly optimal Nash equilibrium for classes of deterministic and stochastic nonlinear differential games is obtained using differential neural networks.…”

Input-Output Stability for Differential Neural Networks

“…Currently, differential neural networks (DfNN) have proven to be an effective tool in the identification, state estimation and control of complex nonlinear systems (see [1], [5] and [6]). …”