Theoretical foundations of automated synthesis using Bond-Graphs and genetic programming

Abstract

Automated synthesis refers to design of physical systems using any of the models proposed for machine intelligence like evolutionary computation, neural networks and fuzzy logic. Mechatronic systems are mixed or hybrid systems as they combine elements from different energy domains. These dynamic systems are inherently complex and capturing underlying energy behavior among interacting sub-systems is difficult owing to the variety in the composition of the mechatronic systems and also due to the limitation imposed by conventional modeling techniques unable to handle more than one energy domain. Bond-graph modeling and simulation is an advanced domain independent, object oriented and polymorphic graphical description of physical systems. The universal modeling paradigm offered by bond-graphs is well suited for mechatronic systems as it can represent their multi energy domain character using a unified notation scheme. Genetic programming is one of the most promising evolutionary computation techniques. The genetic programming paradigm is modeled on Darwinian concepts of evolution and natural selection. Genetic programming starts from a high level statement of a problem's requirements along with a fitness criterion and attempts to produce a computer program that provides a solution to the problem. Combining unified modeling and analysis tools offered by bond-graphs with topologically open ended synthesis and search capability of genetic programming, a novel automated design methodology has been developed for generating mechatronic systems designs using an integrated synthesis, analysis and feedback scheme which comes close to the definition of a true automated invention machine. This research paper develops a theoretical foundation for automated synthesis and design of mechatronic systems using bond-graphs and genetic programming.