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（例如运输系统，网络机器人技术，传感器网络和生物系统）的兴趣越来越多。在上述应用程序中，典型系统由一组子系统组成。每个子系统还具有嵌入式计算机，并具有独特的属性，例如相互作用的物理和逻辑动力学以及分散的决策。网络嵌入式混合系统固有的是异质不确定性的存在。因此，网络嵌入式混合系统比连续的动态系统要复杂得多，该系统大多数当前的控制理论都基于该系统。网络嵌入式混合系统的复杂性在实时信息推理，控制和安全验证方面提出了重大挑战。由于具有不确定性的此类系统的复杂行为可以作为随机混合系统建模，因此NSF职业研究的目的是开发理论，计算高效的数值算法和随机混合系统的实验测试床，并应用于移动网络嵌入式网络系统（强调空中流动控制）。为了实现目标，该项目的重点是以下主题：随机混合系统的实时混合估计和信息推理算法和分析方法。为了计算效率，算法基于分析公式而不是基于样本的估计算法。正在开发基于差异转换的计算有效的数值算法，以最佳控制混合系统，以及可及的集合计算。 开发的算法正在实验测试平台上进行验证，包括自动驾驶汽车。