Neural Networks for Control of Nonlinear Dynamical Systems and Manufacturing Processes

这个项目将研究神经网络，（NN），用于非线性动力系统的控制。它的目标是为大型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（例如输入-输出稳定性和被动概念在60年代由pi之一引入）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值（意味着有界输入）方面的性能。将给出神经网络控制器的可重构设计算法。如果不保证性能和合理的设计算法，神经网络控制器将不会被控制系统社区或美国工业所接受。神经网络控制器有潜力显著改善美国的制造过程控制，因为它们是无模型的，不需要工厂的明确动态。UTA的工作将在自动化和机器人研究所（ARRI）完成，以便技术向工业转移。开发的神经网络控制器将在柔性连杆系统试验台上实施，然后安装在制造表面精加工站上。ARRI表面处理协会已经提供了相应的资金来资助这种工业应用所需的广泛技术工作。国际扶轮研究所经理J. M. Fitzgerald、体育主任和刘凯博士将被分配配合基金来监督这个计划。

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.