Intelligent Control of Dynamic Systems

智能系统的目标是将高级指令或规格自动转换为针对环境或“工厂”的低级动作，同时充分利用任何先验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动作之间进行接口；在先验信息和实时信息之间，可以改进；修改或反驳；在离散状态或指令变化和连续信号之间；在归纳学习和演绎学习之间；在实时行动和离线决策之间；在环境响应的不同时间尺度之间；动作与计算；在模型，算法和架构之间。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，模型类别或模型之间的变化，以响应环境变化，如失效模式或中断；最后在线细化参数，在类内选择模型。在操作层面，由智能系统的核心组成，它必须处理离散和连续变量、数据或动作的定性方面，环境反应、测量或行动的多时间尺度的时间方面和分散或分布式操作、信息或控制的空间方面。在穿越纯离散领域的定性领域中，人们面临着由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引起的问题。随着时间的引入，还可以对性能度量进行建模，例如制造系统中的周期时间或运输系统中的吞吐量。这导致了几个调度和优化问题。最后是智能系统的实现层，它涉及软件、并行或顺序算法的统一，计算机体系结构，并最终将其映射到最终用户的芯片中。因此，这一层产生了一些问题，如数据结构和计算机辅助设计。虽然上面提到了智能系统的三部分剖析，但我们的目标是将其设计成对最终用户透明的，以类似于开放系统互连体系结构的方式。

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.