Dynamic Modeling, Analysis, and Synthesis of Embedded Hybrid Systems

嵌入式系统包括集成到物理过程中的软件组件，正在渗透到我们日常生活的方方面面，从家用电器到安全关键系统，如飞机和核电站。这种广泛的使用要求开发新的工程技术，以确保其及时和可靠的发展，在运行过程中进行准确的监测，并进行强有力的控制，以确保安全和可靠。该项目开发了一种集成的基于模型的嵌入式系统开发方法，包括工厂、其环境和嵌入式计算系统。这些模型为系统稳定性、活跃性、安全性、安全性和实时监控的设计和运行时分析提供了一个通用框架。模型还可以形成用于生成嵌入式系统的硬件和软件组件以及定义其运行时配置的基础。建模的这种生成性是基于模型的开发过程的一个非常相关和独特的属性。此外，在运行时分析系统行为的能力构成了设计用于适应环境中的干扰和意外变化引起的偏差的方法的基础。该项目的目标是确保系统及其周围环境在发生异常情况时不会受到伤害。该项目将开发嵌入式系统的运行时分析技术，以减轻对具有大模式空间的系统进行建模和分析的一些复杂性。具体地说，提出了解决运行时动态分析问题的有效方法。这包括三个主要任务：为具有大模式空间的嵌入式系统模型开发称为动态混合自动机(DHA)的新概念。DHA是一个简单的混合自动机，它可以随着系统行为的演变在运行时增量地构建。它基于混合自动机框架中的形式组合建模技术，确保模型构建作为混合模型中开关元件数量的函数线性增长(而不是指数增长)。使用从混合自动机模型发展而来的混合观测器来跟踪系统行为。这涉及用于在跟踪对象行为的同时检测到模式变化时在线更新观测器的模型的技术。研究挑战集中在模型和跟踪过程，以最小化模式转换边界处的不信任，开发代码生成系统，允许在满足生成过程的硬时间限制的同时增量地重新计算观测器模型，以及在线综合监控控制器以响应模式变化，其中一些变化可能归因于干扰和环境的意外变化。提出了主动控制器模型(ACM)的新概念。ACM是一种动态数据结构，它显式地表示当前有效的监控控制器(SVC)及其发电机和执行器。SVC可以被实现为一个通用过程，它使用ACM作为其“知识库”来计算要采取的控制措施。当工厂模型更改时，ACM将更新以应对新情况。这将涉及许多创新的研究任务，例如开发一种描述控制对象的表达语言，ACM模型的定义和增量更新过程，以及用于在线综合监控控制器代码的“随时”重新源代码约束算法。稳健的监控控制器将把自适应控制的概念扩展到混杂系统领域，并根据工厂和环境的配置变化进行调整。这个项目的所有三个组成部分的成功在很大程度上取决于处理增量模型生成、混合观测器的代码生成以及基于工厂期望目标的在线监控控制器综合中的计算复杂性问题。因此，对综合算法和代码生成算法的复杂性研究是该项目的重要组成部分。这些目标雄心勃勃，但这些方法的成功将在嵌入式应用程序中提供新的灵活性，同时解决运行时操作的可靠性和安全性问题。

Biswas, GautamCCR-0208799Embedded systems, which include software components integrated into physical processes, are per-vading all aspects of our daily lives from home appliances to safety-critical systems, such as aircraft and nuclear plants. The widespread use necessitates the development of new engineering tech-niques that can ensure their timely and assured development, accurate monitoring during operation, and robust control, to ensure safety and reliability. This project develops an integrated model-based approach to embedded system development that includes the plant, its environment, and the embed-ded computing system. The models provide a common framework for design and run-time analyses of system stability, liveliness, safety, security, and real-time supervisory control. Models can also form the basis for generating the hardware and software components of the embedded systems, and de-fining their run-time configurations. This generative aspect of modeling is a very relevant and distin-guishing property of the model-based development process. Further, the ability to analyze system behavior at run-time forms the basis for methodologies designed to accommodate deviations caused by disturbances and unexpected changes in the environment. The goal is to ensure that the system and its surroundings are not harmed when aberrant situations occur.The project will develop technologies for run-time analysis of embedded systems that alleviate some of the complexities of modeling and analysis of systems with large mode spaces. Specifically, effec-tive methodologies that address run-time dynamic analysis issues are addressed. This includes three primary tasks:Developing a new concept called the dynamic hybrid automaton (DHA) for embedded sys-tems models with large mode spaces. A DHA is simply a hybrid automaton, which can be constructed incrementally, on-the-fly, at run-time, as system behavior evolves. It is based on formal compositional modeling techniques in the hybrid automata framework that ensure model construction grows linearly (as opposed to exponentially) as a function of the number of switching elements in the hybrid model. Tracking system behavior using hybrid observers developed from the hybrid automata mod-els. This involves techniques for updating the models of the observer on-line when a mode change is detected while tracking the plant behavior. Research challenges focus on model and tracking procedures that minimize mistracking at mode transition boundaries, and devel-oping code generation systems that allow for incremental recalculation of the observer mod-els while satisfying hard time bounds on the generation process, and Synthesizing supervisory controllers on-line in response to mode changes, some of which may be attributed to disturbances and unexpected changes in the environment. A new con-cept, the Active Controller Model (ACM), is proposed. The ACM is a dynamic data structure that explicitly represents the currently active supervisory controller (SVC), together with its generator and actuator. The SVC can be implemented as a generic procedure that uses the ACM as its "knowledge base" to compute what control actions to take. When the plant model changes, the ACM is updated to address the new situation. This will involve a number of in-novative research tasks, such as developing an expressive language to describe control ob-jectives, definition and incremental update procedures for the ACM models, and "anytime" re-source-bound algorithms for synthesizing supervisory controller code on-line. Robust super-visory controllers will extend the concept of adaptive control into the hybrid-systems domain, and adjust to configuration changes in the plant and environment. The success of all three components of this project is very heavily dependent on handling computa-tional complexity issues in incremental model generation, code generation for the hybrid observer, and on-line supervisory controller synthesis based on desired objectives for the plant. Therefore, complexity studies of the synthesis and code generation algorithms is an important component of the project. The goals are ambitious, but the success of these methods will offer new flexibility in embed-ded applications while addressing issues of reliability and safety during run-time operation.