AIS: Nonlinear Statistical Control Using Neural Networks

AIS：使用神经网络的非线性统计控制

• 批准号: 0969430
• 负责人: Chang-hee Won
• 金额: $ 35万
• 依托单位: Temple University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-01 至 2014-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0969430&HistoricalAwards=false
• 关键词: AIS; Nonlinear; Statistical; Control; Using

AbstractThe objective of this research is to discover new fundamental theories and algorithms for stochastic optimal control with application in solar energy transmission. The approach is based on novel statistical control with cost cumulants and neural networks. In statistical control, one views the performance index as a random variable and shapes the distribution of the performance index. By shaping the performance index, more accurate control performance is achieved.Intellectual Merit. A new optimal control paradigm of performance shaping is proposed. This paradigm will lead to a new direction of research in optimal control for the general nonlinear stochastic systems. By using artificial neural networks, this project proposes to solve the nonlinear optimal control problems. Nonlinear statistical control will allow high performance controllers for various applications. This transformative optimal control theory is applied to a high societal impact application of space solar power harvesting. Statistical control will significantly improve the solar power transmission performance.Broader Impacts. This project will advance the fundamental knowledge in optimal stochastic control. By applying a new optimal control method to the space solar power transmission application, the energy will be transmitted more accurately, which means that the energy is harvested more economically. This research will allow efficient space solar energy transmission by addressing the antenna pointing control problem. Space solar energy harvesting is an issue of national strategic importance. Furthermore, through this project and spacecraft systems engineering course, students of underrepresented groups will be educated about engineering and state-of-the-art research.

摘要本研究的目的是探索随机最优控制的新的基础理论和算法，并将其应用于太阳能的传输。 该方法是基于新的统计控制与成本累积量和神经网络。 在统计控制中，人们将性能指标视为随机变量，并塑造性能指标的分布。 通过设定性能指标，实现更精确的控制性能。智能优点 提出了一种新的性能塑造最优控制范式。 这将为一般非线性随机系统的最优控制研究开辟一个新的方向。 本计画提出利用人工类神经网路来解决非线性最佳控制问题。 非线性统计控制将允许用于各种应用的高性能控制器。 这种变革性的最优控制理论应用于空间太阳能发电的高社会影响应用。 统计控制将显著提高太阳能发电的传输性能。 本计画将增进最佳随机控制之基础知识。 通过将一种新的最优控制方法应用于空间太阳能输电应用中，可以更精确地传输能量，这意味着更经济地收集能量。 这项研究将通过解决天线指向控制问题来实现高效的空间太阳能传输。 空间太阳能收集是一个具有国家战略重要性的问题。 此外，通过这一项目和航天器系统工程课程，代表性不足群体的学生将接受工程和最先进研究方面的教育。