Communications in Embedded Control Systems

嵌入式控制系统中的通信-项目摘要本项目调查不同的嵌入式软件和硬件组件应该如何相互通信，以使整个全球系统以令人满意的方式运行。主要的研究领域是传输信号的信息理论内容，即研究为了使物理系统满足其规格而需要在不同组件之间传输多少位的信息。例如，自主机器人系统通常依赖于各种不同的传感器，然后问题就变成了，需要哪种传感器？此外，是否有可能以系统的方式压缩数据，例如使用虚拟传感器，以便丢弃对当前任务或操作模式不重要的信息？由于完成某项任务所需的感觉数据取决于控制律、系统的动力学和任务的复杂性，信息论提供了一种工具，用于以统一的方式处理有关传感器和执行器选择和控制模式设计的问题。本项目侧重于以下两个有利的研究领域：1.对用于控制连续机械设备的计算机生成的符号输入的信息理论内容进行建模。多智能体机器人系统的嵌入式传感器和执行器的选择。这种选择是为了使控制系统在存在带宽限制的情况下满足其规范，即机器人在避开环境中的障碍物的同时保持队形。在该项目中，计算机生成的或语言控制命令被建模为可由基于触发器的混合系统读取的模式描述串。换言之，基于软件的物理、连续设备控制模型的开发使得实际控制信号在代码长度方面具有明确的信息论内容，从而将多模式控制领域与信息论领域联系起来。该模型使得研究组件集成问题成为可能。由于每个传感器或执行器对编码控制信号所需的总比特数有贡献，因此通过在系统应以令人满意的方式运行的约束下最小化代码长度，得出在当前操作模式中需要什么组件的自然测量。此外，对代码长度的明确关注直接影响到编码理论，即如何表示不同嵌入组件之间共享的信息的理论是该研究的直接结果。本项目正在研究的控制理论的信息论方法在嵌入式控制领域之外具有许多潜在的应用。例如，在遥控机器人中，控制信号是通过通信通道传输的，其中通道噪声的存在使其优选地传输尽可能短的指令。一个相关的问题出现在最小注意控制领域，其中注意函数被定义为控制变异性的度量。

Egerstedt, MagnusCCR-0207411Communications in Embedded Control Systems - Project SummaryThis project investigates how different embedded software and hardwarecomponents should communicate with each other in order for the overall, globalsystem to behave in a satisfactory way. The main area of investigation is theinformation theoretic content of the transmitted signals, i.e. a study of howmany bits of information need to be transmitted between the different componentsin order to make the physical system meet its specifications. For instance,autonomous robotic systems are normally relaying on a variety of heterogeneoussensors, and the question then becomes, which of the sensors are needed? Andfurthermore, is it possible to compress the data in a systematic way, e.g. usingvirtual sensors, so that information that is not essential to the current task,or mode of operation, can be discarded?Since the sensory data needed for accomplishing a certain task depends both onthe control law, the dynamics of the system, and the complexity of the task,information theory provides a tool for treating questions concerning sensor andactuator selection and control mode design in a unified way. This project isfocused on the following two enabling areas of research:1. Modeling of the information theoretic content of the symbolic, computergenerated inputs used for controlling continuous, mechanical devices.2. Selection of embedded sensors and actuators for a multi-agent, roboticsystem. This selection is done so that the control system meets itsspecifications in the presence of bandwidth constraints, i.e. so the robotsmaintain formation while avoiding obstacles in the environment.In this project computer generated, or linguistic control commands are modeledas strings of mode descriptions that can be read by trigger based hybridsystems. In other words, models for software based control of physical,continuous devices are developed in such a way that the actual control signalshave a clear information theoretic content in terms of code lengths, thusconnecting the field of multi-modal control with that of information theory.This model enables the investigation of component integration issues. Since eachsensor or actuator contributes to the total number of bits needed for coding thecontrol signals, by minimizing the code lengths subject to the constraint thatthe system should behave in a satisfactory way, a natural measure of whatcomponents are needed in the current mode of operation is derived. Furthermore,the explicit focus on code lengths has immediate implication on coding theory,i.e. a theory for how the information shared between different embeddedcomponents should be represented is a direct consequence of this research.The information theoretic approach to control theory under investigation in thisproject has a number of potential applications outside the field of embeddedcontrol. For instance, in teleoperated robotics the control signals aretransmitted over communication channels in which the presence of channel noisemakes it preferable to transmit instructions that are as short as possible. Arelated problem arises in the area of minimum attention control, where anattention functional is defined as a measure of the control variability.