Computational Synthesis of Multi-Domain Systems

Computational Synthesis: From Basic Building Blocks to High Level Functionality

Volume

Papers from the 2003 AAAI Spring Symposium

Contents

Several challenging issues have to be addressed for automated synthesis of multi-domain systems. First, design of interdisciplinary (multi-domain) engineering systems, such as mechatronic systems, differs from design of single-domain systems, such as electronic circuits, mechanisms, and fluid power systems, in part because of the need to integrate the several distinct domain characteristics in predicting system behavior. Second, a mechanism is needed to automatically select useful elements from the building block repertoire, construct them into a system, evaluate the system and then reconfigure the system structure to achieve better performance. Dynamic system models based on diverse branches of engineering science can be expressed using the notation of bond graphs, based on energy and information flow. One may construct models of electrical, mechanical, magnetic, hydraulic, pneumatic, thermal, and other systems using only a rather small set of ideal elements as building blocks. Another useful tool, genetic programming, is a powerful method for creating and evolving novel design structures in an open-ended manner. Through definition of a set of constructor functions, a genotype tree is created for each individual in each generation. The process of evaluating the genotype tree maps the genotype into a phenotype -- i.e., to the abstract topological description of the design of a multi-domain system, using a bond graph along with parameters for each component, if needed. Finally, physical realization is carried out to relate each abstract element of the bond graph to corresponding components in various physical domains. To implement the above GPBG approach in a specific application domain, cautious steps have to be taken to make the evolved design represented by bond graphs realizable and manufacturable. To achieve this, one important step is to define appropriate building blocks of the design space and carefully design a realizable function set in genetic programming. We are going to illustrate this in an example of behavioral synthesis of a RF MEM circuit -- a micro-mechanical band pass filter design. Finally, we have some discussions on how to extend the above approach to an integrated evolutionary synthesis environment for MEMS across a variety of design layers.