Intelligent Control of Dynamic Systems

动态系统的智能控制

• 批准号: 9216487
• 负责人: William Perkins
• 金额: $ 20万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-09-15 至 1996-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9216487&HistoricalAwards=false
• 关键词: Intelligent; Control; Dynamic; Systems

The goal of an intelligent system is to translate high level commands or specifications automatically into lower level actions upon the environment or "plant," while fully utilizing any prior information as well as the information contained in the real time environmental responses. Such an intelligent system must interface between high level specifications and low level actions; between prior information and real-time information which may improve; modify or contradict it; between discrete state or command changes and continuous signals; between inductive and deductive learning; between real-time actions and off-line decisions; between various time scales of environmental response; action and computation; and between models, algorithms, and architectures. At the highest level, the intelligent system must be able to select classes of models within which to describe the environment, change between model classes or models in response to environmental changes such as failure modes or disruptions, and finally refine the parameters on-line to select a model within a class. At the operational level, consisting of the core of an intelligent system, it must deal with the qualitative aspects of discrete and continuous variables, data or actions, the temporal aspects of multiple-time scales of environmental response, measurement or action and the spatial aspects of decentralized or distributed operation, information or control. In the qualitative realm traversing the purely discrete domain, one has the issues arising from automata theory and supervisory control for reliable or safe operation of discrete event dynamics. With the introduction of time, one also has the ability to model performance measures such as cycle-time in manufacturing systems or throughput in transportation systems. This leads to several scheduling and optimization problems. Finally, one has the implementation layer of an intelligent system which involves a unification of software, algorithms whether parallel or sequential, computer architectures and ultimately their mapping into chips for the end user. Thus this layer gives rise to several issues such as data structures and computer-aided-design. While the above suggests a tripartite dissection of an intelligent system, our goal is to nevertheless design it to be transparent to the end user, in a fashion similar to the open systems interconnection architecture.

智能系统的目标是将高级命令或规范自动转换为针对环境或“工厂”的较低级操作，同时充分利用任何先前信息以及实时环境响应中包含的信息。 这样的智能系统必须在高级规范和低级操作之间建立接口；先前信息和可能改进的实时信息之间的关系；修改或反驳； 离散状态或命令变化与连续信号之间； 归纳学习和演绎学习之间；实时行动和离线决策之间；环境响应的不同时间尺度之间；行动和计算； 以及模型、算法和架构之间的关系。 在最高层次上，智能系统必须能够选择模型类别来描述环境，在模型类别或模型之间进行更改以响应环境变化（例如故障模式或中断），最后在线细化参数以选择一个类中的模型。 在操作层面，作为智能系统的核心，它必须处理离散和连续变量、数据或行动的定性方面，环境响应、测量或行动的多时间尺度的时间方面以及空间方面分散或分布式操作、信息或控制。 在穿越纯离散领域的定性领域中，人们面临着自动机理论和离散事件动力学可靠或安全运行的监督控制所引起的问题。 随着时间的引入，人们还能够对性能指标进行建模，例如制造系统中的周期时间或运输系统中的吞吐量。 这导致了一些调度和优化问题。 最后，智能系统的实现层涉及软件、并行或顺序算法、计算机架构的统一，以及最终将它们映射到最终用户的芯片中。 因此，这一层引起了数据结构和计算机辅助设计等几个问题。 虽然上面建议对智能系统进行三方剖析，但我们的目标是以类似于开放系统互连架构的方式将其设计为对最终用户透明。