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

• 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

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 Lewis 该项目将研究神经网络 (NN)，用于控制非线性动力系统。 其目标是为大类非线性系统的稳定神经网络控制器的可重复设计提供一个框架。 它将使用非线性稳定性理论（例如一位 PI 在 60 年代引入的输入输出稳定性和无源性概念）来研究神经网络的近似特性，重点是保证神经网络控制器在小跟踪误差和有界神经网络权重方面的性能（意味着有界输入）。 将为神经网络控制器提供可重复的设计算法。 如果没有性能和合理设计算法的保证，神经网络控制器将理所当然地不会被控制系统社区或美国工业界所接受。 神经网络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，并且不需要工厂的明确动态。 UTA 工作将在自动化和机器人研究所 (ARRI) 完成，以便将技术转移到工业界。 开发的神经网络控制器将在 Flexively-Link Systems Testbed 上实施，然后安装在制造表面精加工站上。 ARRI表面精加工联盟已提供配套资金来资助该工业应用所需的广泛技术工作。 ARRI经理J. M. Fitzgerald, PE和刘凯博士将在配套资金的基础上被指派来监督这个项目。