CPS: Synergy: Collaborative Research: Computationally Aware Cyber-Physical Systems

CPS：协同：协作研究：计算感知网络物理系统

• 批准号: 1544396
• 负责人: Ricardo Sanfelice
• 金额: $ 43.2万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1544396&HistoricalAwards=false
• 关键词: CPS; Synergy; Collaborative; Research; Computationally

The objective of this work is to generate new fundamental science that enables the operation of cyber-physical systems through complex environments. Predicting how a system will behave in the future requires more computing power if that system is complex. Navigating through environments with many obstacles could require significant computing time, which may delay the issue of decisions that have to be made by the on-board algorithms. Fortunately, systems do not always need the most accurate model to predict their behavior. This project develops new theory for deciding between the best model to use when making a decision in real time. The approach involves switching between different predictive models of the system, depending on the computational burden of the associated controller, and the accuracy that the predictive model provides. These tools will pave the way for more kinds of aircraft to navigate closely and safely with one another through the National Air Space (NAS), including Unmanned Air Systems (UAS). 

The results from this project will enable more accurate and faster trajectory synthesis for controllers with nonlinear plants, or nonlinear constraints that encode obstacles. The approach utilizes hybrid control to switch between models whose accuracy is normalized by their computational burden of predictive control methods. This synergistic approach enables computationally-aware cyber-physical systems (CPSs), in which model accuracy can be jointly considered with computational requirements. The project advances the knowledge on modeling, analysis, and design of CPSs that utilize predictive methods for trajectory synthesis under constraints in real-time cyber-physical systems. 
The results will include methods for the design of algorithms that adapt to the computational limitations of autonomous and semi-autonomous systems while satisfying stringent timing and safety requirements. With these methods come new tools to account for computational capabilities in real-time, and new hybrid feedback algorithms and prediction schemes that exploit computational capabilities to arrive at more accurate predictions within the time constraints. The algorithms will be modeled in terms of hybrid dynamical systems, to guarantee dynamical properties of interest. The problem space will draw from models of UAS in the NAS.

这项工作的目的是生成新的基本科学，以通过复杂的环境实现网络物理系统的运行。如果该系统很复杂，那么预测系统将来的行为将需要更多的计算能力。在具有许多障碍的环境中导航可能需要大量的计算时间，这可能会延迟板载算法必须做出的决策问题。幸运的是，系统并不总是需要最准确的模型来预测其行为。该项目开发了新的理论，可以在实时做出决定时决定使用的最佳模型。该方法涉及在系统的不同预测模型之间切换，具体取决于关联控制器的计算负担以及预测模型提供的准确性。这些工具将为更多类型的飞机铺平道路，以通过国家空间（NAS）（包括无人空气系统（UAS））彼此紧密安全地航行。 ＆＃8232;＆＃8232;该项目的结果将使具有非线性植物的控制器或编码障碍物的非线性约束，可以使更准确，更快的轨迹合成。该方法利用混合控制来切换其准确性通过其预测控制方法计算负担归一化的模型。这种协同方法可实现计算意识的网络物理系统（CPSS），其中可以共同考虑具有计算要求的模型精度。该项目在实时网络物理系统中使用预测方法的CPS的建模，分析和设计知识提高了知识。 ＆＃8232;结果将包括设计适应自主和半自主系统计算限制的算法的方法，同时满足严格的时机和安全要求。通过这些方法，新的工具可以实时考虑计算能力，以及利用计算能力以在时间限制内实现更准确的预测的新的混合反馈算法和预测方案。该算法将根据混合动力学系统进行建模，以确保感兴趣的动力学特性。问题空间将来自NAS中的UAS模型。