A new hybrid hierarchy model description method

Abstract

As requirements in diagnosis for hybrid systems increase, more and more researchers concentrate on hybrid models. However, common visual modeling methods such as GME (General Modeling Environment) lacks of flexibility. There is no appropriate modeling method for hybrid systems in cases containing plenty of complex components. This paper proposes a new hybrid hierarchy model description method, LLSM (Language for Large-Scale Modeling), based on concurrent probabilistic hybrid automata (cPHA) to make the process expediently. LLSM describes systems in the form of text. It settles the problem in three aspects: granularity, hierarchy and reusability. Component-oriented modeling of LLSM helps control granularity easily allowing users to create models in different scales. A special mark, which is employed to represent hierarchical relationship makes the system clearer and guides the accuracy of diagnosis. Reusability is achieved by C-style grammar which indicates component libraries for large-scale applications. In complex applications, LLSM creates models efficiently by existing libraries in the form of collaboration. Test on a switch demonstrates how it works.