SBIR Phase I: Recurrent Adaptive Critics for Nonlinear Systems Control

SBIR 第一阶段：非线性系统控制的循环自适应批评

• 批准号: 9660604
• 负责人: Richard Saeks
• 金额: $ 7.5万
• 依托单位: Accurate Automation Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-01-01 至 1997-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9660604&HistoricalAwards=false
• 关键词: SBIR; Phase; Recurrent; Adaptive; Critics

*** ABSTRACT 9660604 Mathia This Small Business Innovation Research Phase I project will develop a practical methodology for applying to real world engineering applications new nonlinear adaptive critic principals which incorporate recurrent neural networks. Accurate Automation has recently developed a powerful new theory which guarantees stable adaptive critic control systems. This theory describes certain properties that the subsystems of the adaptive critic algorithm (action, model, critic) and their associated learning algorithms must have to ensure stable control. Recurrent neural networks are a powerful tool which can be applied to realize these properties. We will apply our theory to the development of a theoretical and experimental basis for understanding adaptive critics with recurrent neural network subsystems. The algorithm will learn 'on-line' to control a highly nonlinear system, with stability guaranteed and optimal performance guaranteed in the limit. Phase I will result in prototype of a commercializable software system. In Phase II, we will develop special purpose hardware for sale. The adaptive critic under development in this research will be targeted specifically for application in aircraft, making the aircraft industry the most obvious commercial arena for this technology. Because the adaptive critic algorithm is very general, it will also have applications in any industry requiring good nonlinear controls. Examples of these industries are the chemical industry, the automobile industry, and the photocopier industry. ***

马蒂亚：这个小企业创新研究第一阶段的项目将开发一种实用的方法，将新的非线性自适应批评原则应用于现实世界的工程应用，其中包括循环神经网络。精确自动化最近发展了一个强大的新理论，保证稳定的自适应临界控制系统。该理论描述了自适应批评算法的子系统（动作、模型、批评）及其相关学习算法必须具备的某些特性，以确保稳定的控制。递归神经网络是实现这些特性的有力工具。我们将把我们的理论应用于理论和实验基础的发展，以理解递归神经网络子系统的自适应批评。该算法将学习“在线”控制高度非线性系统，在极限下保证稳定性和最优性能。第一阶段将产生可商业化软件系统的原型。在第二阶段，我们将开发销售专用硬件。本研究中正在开发的自适应批评家将专门针对飞机应用，使飞机工业成为该技术最明显的商业领域。由于自适应批评算法是非常通用的，它也将在任何需要良好的非线性控制的行业中得到应用。这些工业的例子是化学工业、汽车工业和影印工业。＊＊＊