Compact Representations for Adaptive Critic Designs

【摘要】基于值的分布式系统控制的紧凑值函数本项目将扩展P1先前在NSF关于分布式系统智能控制的资助上的工作。之前的拨款源于基于价值的智能控制的最新成果，有时也被称为近似动态规划（ADP）或强化学习（RL）。在某些理论假设下，旧形式的控制通常保证稳定性，但不关注最优性能。新方法侧重于优化性能，而不需要假设线性或系统动力学的完美知识。通过使用新的基于模型的ADP或RI版本，并通过开发适合分布式系统的值近似方案，他能够在分布式任务（如流体流动控制和导弹拦截）的测试问题中展示新的性能水平。这个新项目旨在成为解决更大、更普遍的分布式控制问题的第一阶段，如制造过程、作业调度、网络流量等。第一阶段将是探索更强大的数值逼近方案，以增强控制由偏微分方程控制的系统的能力，例如在航空航天，等离子体和工业应用中遇到的流体流动，在可能的小规模探索工作中。

ABSTRACTCompact Value Functions for Value-Based Control of Distributed SystemsThis project will extend the P1's work on a previous NSF grant on intelligent control for distributed systems. That previous grant started from recent results in value-based intelligent control, sometimes called approximate dynamic programming (ADP) or reinforcement learning (RL). Older forms of control typically guarantee stability, under certain theoretical assumptions, but do not focus on optimal performance. The new methods focus on optimal performance, without assuming linearity or perfect knowledge of system dynamics.By using new model-based versions of ADP or RI, and by developing value approximation schemes suitable for distributed systems, he was able to demonstrate new levels of performance in test problems in distributed tasks such as fluid flow control and missile interception. The new project is intended to be the first stage in addressing larger and more general distributed control problems, such as manufacturing processes, job scheduling, network traffic, etc. The first stage will be to explore more powerful value approximation schemes, in order to enhance the ability to control systems governed by partial differential equations, such as fluid flows encountered in aerospace, plasma and industrial applications, to the extent possible in a small-scale exploratory effort.