Hamiltonian Critic Based Controllers for Stochastic Systems

本项目将研究9313946个基于Balakrishnan人工神经网络的非线性系统控制器。这个项目的两个主要目标是基于不确定非线性系统的自适应批评设计和随机表示来构造神经网络控制器。最优控制问题中的哈密顿量及其导数是在自适应批评设计中提出的。基于邻域最优控制理论，提出了一种恒定网络配置控制器和一种在线修正网络配置控制器。提出了基于滤波理论的对象随机表示方法。此外，还描述了建议对这些网络控制器进行研究的问题类别。希望这一研究结果将大大提高对基于自适应批评者的神经控制器和不确定非线性系统的随机神经网络表示的理解。***

9313946 Balakrishnan Artificial neural network based controllers for nonlinear systems will be studied int he project. Two main objectives of this project are to formulate neural network controllers based on adaptive critic designs and stochastic representations of uncertain nonlinear systems. Hamiltonian and its derivatives which occur in the formulation of optimal control problems are proposed in the adaptive critic design. A constant network configuration controller and an online-modified network configuration controllers are proposed based on neighboring optimal control theory. The stochastic representations of a plant are proposed to be based on filtering theory. Furthermore, classes of problems are described on which these network controllers are proposed to be investigated. It is hoped that the results of this study will substantially improve understanding of the adaptive critic based neuro controllers and the stochastic neural network representations of uncertain nonlinear systems. ***