Motivating Hierarchical Run-Time Models in Measurement and Control Systems

Jie Liu, Stan Jefferson, and Edward A. Lee

2001 American Control Conference
June 25 - 27, 2001 Arlington, VA, USA pp. 3457-3462

Prepublished version
Published version


Measurement and control systems are intrinsically distributed and real-time, as they consist sensor and actuator nodes that interact with the physical world directly. Embedded software in the computational nodes is responsible for timely reacting to sensor data and producing actuation. This paper reviews run-time computation models for this kind of real-time embedded software, from message semantics, message acquisition, and the dataflow/control-flow perspectives. In general, dataflow centric models are natural for describing measurement and control algorithms and easy to use in distributed systems, but they lack mechanisms of controlling the execution order to fulfill timing constrains. Control-flow centric models are good at handling real-time requirements but are hard to distribute and sometimes hard to analysis. Although most practical run-time models to some extent support both dataflow and control-flow, they are hardly universal. In complex applications, it is desirable to use different models in different parts of the system or under different modes of operation. Cleanly integrating multiple run-time models is a challenging task. In this paper, we motivate a hierarchical architecture for composing run-time models, based on the Ptolemy II component framework and models of computation. Unlike traditional real-time operating systems that provide only one flat layer of abstraction, the hierarchical architecture enhances flexibility, scalability, and reliability of MC systems by mixing and match multiple run-time models.