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Model-based automatic code generation is a process of converting abstract models into concrete implementations in the form of a program written in a high-level programming language. The process consists of two steps, first translating the primitives of the model into (approximately) equivalent implementations, and then scheduling the implementations of primitives according to the data dependency inherent in the model. When the model is based on hybrid automata that combine continuous dynamics with a finite state machine, the data dependency must be viewed in two aspects: continuous and discrete. Continuous data dependency is present between mathematical equations modeling timecontinuous behavior of the system. On the other hand, discrete data dependency is present between guarded transitions that instantaneously change the continuous behavior of the system. While discrete data dependency has been studied in the context of code generation from modeling languages with synchronous semantics (e.g., ESTEREL), there has been no prior work that addresses both kinds of dependency in a single framework. In this paper, we propose a code generation framework for hybrid automata which deals with continuous and discrete data dependency. We also propose techniques for generating modular code that retains modularity of the original model. The framework has been implemented based on the hybrid system modeling language CHARON, and experimented with Sony’s robot platform AIBO.
Jesung Kim and Insup Lee, "Modular Code Generation from Hybrid Automata based on Data Dependency", . May 2003.
Date Posted: 09 November 2004
This document has been peer reviewed.