Adaptive Critic Based Neurocontrol for Distributed Parameter Systems

分布式参数系统的基于自适应批评的神经控制

• 批准号: 9976588
• 负责人: Sivasubramanya Balakrishnan
• 金额: $ 31.49万
• 依托单位: Missouri University of Science and Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-09-01 至 2003-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9976588&HistoricalAwards=false
• 关键词: Adaptive; Critic; Based; Neurocontrol; Distributed

9976588BalakrishnanThis project will extend the adaptive critic designs in neural networks to the control of distributed parameter systems. One of the strengths of the adaptive critic design is that it presents a unified learning based approach regardless of the problem structure-whether it is linear or nonlinear. This research has two major objectives: the first is to develop two adaptive critic designs for the control of distributed parameter systems. The first design based on approximate dynamic programming outputs optimal return function and the derivatives of the cost with respect to the system states. The second critic design outputs the Hamiltonian and its derivatives with respect to the states and control. The second major objective is to analyze performance of these neuro-controllers in controlling parabolic (and hyperbolic) and elliptic systems. These types of systems represent distributed parameter control problems in MEMS, active flow control, and active vibration control, ocean mining for minerals, power systems, etc. We will consider regulator problems AND finite horizon problems. The stability and convergence of the adaptive critic designs will form part of the study. He plans investigate the effects of imperfect modeling which is a crucial issue in any controller synthesis for distributed parameter systems. The significance of this research lies in the fact that it combines the human problem solving (brain like intelligence) philosophy embedded in the adaptive critic design with classical mathematics to solve some very important and current complex problems in Engineering.***

9976588Balakrishnan该项目将扩展神经网络中的自适应批评设计到分布参数系统的控制。 自适应批评设计的优点之一是，它提出了一个统一的学习为基础的方法，无论问题的结构是线性或非线性。 本研究有两个主要目的：第一是发展两种适用于分布参数系统控制的自适应临界设计。基于近似动态规划的第一种设计输出最优收益函数和成本对系统状态的导数。第二个批评者设计输出相对于状态和控制的汉密尔顿算子及其导数。 第二个主要目标是分析这些神经控制器在控制抛物（和双曲）和椭圆系统的性能。这些类型的系统代表分布参数控制问题的MEMS，主动流量控制，主动振动控制，海洋采矿矿物，电力系统等，我们将考虑调节器问题和有限时域问题。自适应批评设计的稳定性和收敛性将成为研究的一部分。 他计划研究不完美建模的影响，这是分布参数系统控制器综合中的一个关键问题。 这项研究的意义在于它将自适应批判设计中嵌入的人类问题解决（类脑智能）哲学与经典数学相结合，以解决工程中一些非常重要和当前复杂的问题。