Neural Networks for Control of Nonlinear Dynamical Systems and Manufacturing Processes

用于控制非线性动力系统和制造过程的神经网络

基本信息

批准号：
9521673
负责人：
Frank Lewis
金额：
$ 16.86万
依托单位：
University of Texas at Arlington
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
1995
资助国家：
美国
起止时间：
1995-09-15 至 2001-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=9521673&HistoricalAwards=false
关键词：
Neural Networks Control Nonlinear Dynamical

项目摘要

9521673 Lewis This project will investigate investigate neural network, (NN), for control of nonlinear dynamical systems. Its objective is to provide a framework for repeatable design of stable NN controllers for large classes of nonlinear systems. It will use nonlinear stability theory (e.g. input -output stability and passivity notions introduced in the 60's by one of the PIs) to study approximation properties of NN, with emphasis on guaranteeing NN controller performance in terms of small tracking errors and bounded NN weights (implying bounded inputs). Re eatable design algorithms will be given for NN controllers. Without guaranteed for performance and sensible design algorithms, NN controller s will rightfully not be accepted bye the control system community or US industry. NN controllers have the potential to significantly improve manufacturing process control in the US, since they are model-free and do not require explicit dynamics of the plant. The UTA work will be done at the Automation and robotics Research Institute (ARRI), so that technology transfer to industry will occur. The NN controllers developed will be implemented on the Flexibly-Link Systems Testbed, and then installed on the Manufacturing Surface Finishing Station. The ARRI Surface Finishing Consortium has offered matching money to finance the extensive technical work needed for this industrial application. ARRI Manager J. M. Fitzgerald, PE, and Dr. Kai Liu will be assigned on a matching fund basis to supervise this project.

9521673刘易斯该项目将研究神经网络（NN），以控制非线性动力学系统。它的目标是为大型非线性系统的稳定NN控制器重复设计提供一个框架。它将使用非线性稳定性理论（例如，在60年代由一个PIS引入的输入 - 输出稳定性和无源性概念）来研究NN的近似特性，重点是保证NN控制器的性能在小跟踪错误和有限的NN权重（意味着有界输入）方面（意味着有限输入）。将为NN控制器提供可食用的设计算法。如果没有保证性能和明智的设计算法，则nn控制器将不被控制系统社区或美国行业接受。 NN控制器有可能显着改善美国的制造过程控制，因为它们是无模型的，并且不需要植物的明确动态。 UTA工作将在自动化和机器人研究所（ARRI）上完成，以便将进行技术转移到行业。开发的NN控制器将在灵活的链接系统测试台上实现，然后安装在制造表面装饰站。 Arri Surface Achancortium提供了匹配的资金，以资助此工业应用所需的广泛技术工作。 ARRI经理J. M. Fitzgerald，PE和Kai Liu博士将以匹配的基础为基础分配以监督该项目。