Software and Neural Architectures for Real-Time Adaptive Learning and Control

9412430 Peterson该项目将开发软件和神经生物学连接，这对于在实时学习和控制领域取得进展非常重要。研究将集中于使用Pontryagin最优性和Hamilton-Jacob-Bellman （HJB）方程结合在线（或实时）状态数据测量来建立局部控制律模型的方法。这些技术分享了用于构建控制律模型的自适应批评方法的精神，但与实时问题的前提紧密相连。这里的重点是在三个主要领域对这些技术进行相关的改进和扩展：理论分析、局部范围的人工神经体系结构和面向对象的软件体系结构设计。将开发使用c++语言的通用软件代码来支持这些研究工作。图形界面将使用带有Motif小部件集的X窗口来设计。然后，软件环境将在感兴趣的问题领域进行测试，例如大空间结构（LSS）的降阶模型和柔性机械臂。

9412430 Peterson This project will develop the software and neurobiological connections that are important to making progress in the areas of real-time learning and control. The research will focus on methods using the Pontryagin optimality and Hamilton-Jacob-Bellman (HJB) equation in conjunction with on-line ( or real-time) measurements of state data to build local models of control law. These techniques share the spirit of the adaptive-critic methodologies for construction of models of the control law, but are tightly bound to the premise of real-time problems. The focus here is on pertinent refinements and extensions of these techniques in three major arenas: theoretical analysis, locally scoped artificial neural architecture and object oriented software architecture design. General software code using C++ language will be developed to support these research efforts. The graphical interface will be designed using X windows with the Motif widget set. The software environment will then be tested on problem domains of interest, e.g. reduced order model of Large Space Structures (LSS) and flexible robotic arms.