Dynamic Modeling, Analysis, and Synthesis of Embedded Hybrid Systems

Biswas，GautamCCR-0208799嵌入式系统，包括集成到物理过程中的软件组件，正渗透到我们日常生活的各个方面，从家用电器到安全关键系统，如飞机和核电站。 广泛的使用需要开发新的工程技术，以确保其及时和有保证的开发，在运行期间的准确监测和鲁棒控制，以确保安全性和可靠性。本计画针对嵌入式系统的开发，提出一套整合式的模型化方法，包括工厂、环境与嵌入式运算系统。该模型为系统的稳定性、活跃性、安全性、保密性和实时监控的设计和运行分析提供了一个通用的框架。模型还可以形成生成嵌入式系统的硬件和软件组件的基础，并定义它们的运行时配置。建模的这种生成方面是基于模型的开发过程的一个非常相关和显著的属性。此外，在运行时分析系统行为的能力形成了设计用于适应由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异常情况发生时不受损害。该项目将开发用于嵌入式系统运行时分析的技术，以减轻大模式空间系统建模和分析的复杂性。具体来说，有效的方法，解决运行时的动态分析问题。这包括三个主要任务：开发一个新的概念，称为动态混合自动机（DHA）的嵌入式系统模型与大模式空间。DHA只是一个混合自动机，它可以随着系统行为的发展在运行时增量地、动态地构建。它基于混合自动机框架中的正式组合建模技术，该技术确保模型构造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）。用从混合自动机模型发展的混合观测器跟踪系统行为。这涉及的技术更新的观察员的模型在线时，模式变化被检测到，同时跟踪植物的行为。研究的挑战集中在模型和跟踪程序，最大限度地减少在模式转换边界的误跟踪，并开发代码生成系统，允许增量重新计算的观察员模型，同时满足硬时间限制的生成过程中，和综合监督控制器在线响应模式的变化，其中一些可能是由于干扰和意外的变化，在环境中。提出了主动控制器模型（ACM）的概念。ACM是一种动态数据结构，它显式地表示当前活动的监控控制器（SVC）及其生成器和执行器。SVC可以被实现为通用程序，其使用ACM作为其“知识库”来计算要采取什么控制动作。当被控对象模型发生变化时，ACM将进行更新以应对新的情况。这将涉及一些创新性的研究任务，如开发一种表达语言来描述控制对象，定义和增量更新程序的ACM模型，和“随时”的资源绑定算法的监督控制器代码在线合成。鲁棒超鲁棒控制器将自适应控制的概念扩展到混合系统领域，并根据工厂和环境中的配置变化进行调整。该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和基于工厂所需目标的在线监控控制器合成中的计算复杂性问题。因此，复杂性研究的综合和代码生成算法是本项目的重要组成部分。这些目标是雄心勃勃的，但这些方法的成功将为嵌入式应用程序提供新的灵活性，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.