CAREER: Hybrid Estimation and Real-Time Computational Algorithms for Networked Embedded Hybrid Systems

职业：网络嵌入式混合系统的混合估计和实时计算算法

• 批准号: 0746299
• 负责人: Inseok Hwang
• 金额: $ 32万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-05-01 至 2015-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0746299&HistoricalAwards=false
• 关键词: CAREER; Hybrid; Estimation; Real; Time

Due to advances in embedded systems and communication technologies, there is an increasing interest in networked embedded hybrid system applications such as transportation systems, networked robotics, sensor networks, and biological systems. In the above applications, the typical system consists of a group of subsystems. Each subsystem also has an embedded computer and possesses unique properties such as interacting physical and logical dynamics and decentralized decision making. Inherent to the networked embedded hybrid system is the presence of heterogeneous uncertainty. Thus, a networked embedded hybrid system is much more complex than a continuous dynamic system, on which most current control theory is based. The complexity of a networked embedded hybrid system presents major challenges in the areas of real-time information inference, control, and safety verification. Since the complex behavior of such systems with uncertainties could be modeled as a stochastic hybrid system, the objective of this NSF CAREER research is to develop theory, computationally efficient numerical algorithms, and experimental testbeds for stochastic hybrid systems, with application to mobile networked embedded systems (emphasizing air traffic control). To achieve the objective, this project is focusing on the following topics: real-time hybrid estimation and information inference algorithms and analysis methods for stochastic hybrid systems. For computational efficiency, algorithms are based on analytical formulation instead of widely-used sample-based estimation algorithms; computationally efficient numerical algorithms based on Differential Transformation are being developed for optimal control of hybrid systems, and for reachable set computation. The developed algorithms are being validated on experimental test platforms, including autonomous air vehicles.

由于嵌入式系统和通信技术的进步，人们对网络嵌入式混合系统应用越来越感兴趣，例如运输系统、网络机器人、传感器网络和生物系统。在上述应用程序中，典型的系统由一组子系统组成。每个子系统也有一个嵌入式计算机，并具有独特的属性，如相互作用的物理和逻辑动态和分散的决策。网络化嵌入式混合系统固有的异构不确定性存在。因此，网络嵌入式混合系统比当前大多数控制理论所依据的连续动态系统要复杂得多。网络化嵌入式混合系统的复杂性在实时信息推断、控制和安全验证等方面提出了重大挑战。由于这种具有不确定性的系统的复杂行为可以被建模为随机混合系统，因此NSF CAREER研究的目标是为随机混合系统开发理论、计算效率高的数值算法和实验测试平台，并应用于移动网络嵌入式系统（强调空中交通管制）。为实现这一目标，本项目将重点研究随机混合系统的实时混合估计和信息推理算法及分析方法。为了提高计算效率，算法基于解析公式，而不是广泛使用的基于样本的估计算法；基于微分变换的高效数值算法被用于混合系统的最优控制和可达集计算。开发的算法正在实验测试平台上进行验证，包括自动飞行器。