复杂系统数学模型中的耦合控制与人本自动机
Coupling Control and Human-Centered Automation in Mathematical Models of Complex Systems
摘要：
在本文中，我们从数学上分析了如何将人为因素有效地纳入复杂系统的分析和控制中。作为一个例子，我们集中我们的讨论围绕在智能交通系统（ITS）的理论和实践中的关键问题之一，速度控制的问题，在这里被认为是一个决策过程，有限的信息。这个问题是在数学上铸造的控制问题的一般框架，并在动态变化的环境中，控制耦合到以人为中心的自动化的背景下处理。由于在这种情况下，控制可能不限于少量的控制设置，因为它往往是在控制文献中假设，严重的困难出现在这个问题的解决方案。我们证明，这个问题可以减少到一组的Hamilton-Jacobi-Bellman方程的人的因素，通过系统的哈密顿量的估计。在ITS的上下文中，这些估计可以获得与使用车载设备，如传感器/接收器/执行器，车载通信设备等所提出的方法提供了一种方法来集成人为因素到其他复杂的动态系统的模型的求解过程。
英文摘要：
In this paper we analyze mathematically how human factors can be effectively incorporated into the analysis and control of complex systems. As an example, we focus our discussion around one of the key problems in the Intelligent Transportation Systems (ITS) theory and practice, the problem of speed control, considered here as a decision making process with limited information available. The problem is cast mathematically in the general framework of control problems and is treated in the context of dynamically changing environments where control is coupled to human-centered automation. Since in this case control might not be limited to a small number of control settings, as it is often assumed in the control literature, serious difficulties arise in the solution of this problem. We demonstrate that the problem can be reduced to a set of Hamilton-Jacobi-Bellman equations where human factors are incorporated via estimations of the system Hamiltonian. In the ITS context, these estimations can be obtained with the use of on-board equipment like sensors/receivers/actuators, in-vehicle communication devices, etc. The proposed methodology provides a way to integrate human factor into the solving process of the models for other complex dynamic systems.
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