CAREER: Computational Methods for the Analysis and Design of Stochastic Hybrid Systems

信息和微尺度技术的最新进展正在使新一代嵌入式系统成为可能，这些系统渗透到我们的日常生活中，从汽车和飞机到制造工厂。这些应用程序包括紧密耦合的物理和计算过程，在存在不确定性和可变性的情况下与环境动态交互。本计画旨在发展一个随机混合系统架构，提供分析与设计此类系统的理论、计算方法与软体工具。 随机混合系统近似的局部一致的马尔可夫决策过程，保持当地的均值和协方差。该项目解决了以下关键的研究挑战：（1）使用概率时态逻辑表达的系统属性的形式化分析。随机互模拟提供了等效的近似马尔可夫过程的最小模型，并允许可扩展的算法的发展。(2)应用随机最佳控制设计高度自治之嵌入式控制系统。有效的算法设计，利用随机互模拟，占可用的控制。采用基于模型的软件设计方法，构建了一套支持工具，并进行了实验研究，验证了结果。研究进展被整合到本科和研究生课程使用一系列现实的挑战问题。技术成果、软件工具和教育材料将以电子方式利用开放源模式以及通过出版物和参加会议的方式提供。该项目的更广泛的目标是建立坚实的理论基础和计算方法，这将影响嵌入式系统设计的应用，如移动的传感器网络的国土安全和环境监测，智能交通系统，机电系统和制造业的化学过程。

Recent advances in information and micro-scale technologies are enabling a new generation of embedded systems that are pervading our daily lives, from automobiles and aircrafts to manufacturing plants. Such applications consist of tightly coupled physical and computational processes that dynamically interact with the environment in the presence of uncertainty and variability. This project aims at developing a stochastic hybrid system framework that provides the theories, computational methods, and software tools for analysis and design of such systems. Stochastic hybrid systems are approximated by locally consistent Markov decision processes that preserve local mean and covariance. The project addresses the following critical research challenges: (1) Formal analysis of system properties expressed using probabilistic temporal logics. Stochastic bisimulations are employed to provide equivalent minimal models of the approximating Markov processes and allow the development of scalable algorithms. (2) Design of embedded control systems with high degree of autonomy using stochastic optimal control. Efficient algorithms are designed by utilizing stochastic bisimulations that account for the available controls. Model-based software design is used for building a suite of supporting tools and experimental research is conducted to validate the results. The research advancements are integrated into the undergraduate and graduate curriculum using a series of realistic challenge problems. Technical results, software tools, and the education materials will be made available electronically using an open-source model as well as through publications and conference participation. The broader goal of the project is to establish solid theoretical foundations and computational methods that will impact embedded system design in applications such as mobile sensor networks for homeland security and environmental monitoring, intelligent transportation systems, and electromechanical systems and chemical processes in manufacturing.