Bond graph is the methodology for modeling multidisciplinary dynamic system. They are succinct pictorial statements of mathematical modeling. The traditional modeling and simulation techniques for dynamic systems are generally adequate for single-domain systems only, but the Bond Graph technique provides new strategies for reliable solutions of multi domain system. They are also used for analyzing linear, non linear dynamic production system, artificial intelligence, image processing, robotics and industrial automation. They are domain independent, allow free composition and are efficient for classification and analysis of model, allowing repaid determination of various types of acceptability or feasibility of candidate design.
This research introduces an approach to model order reduction that retains structural information in the reduced order model. Neither traditional methods such as aggregation nor modern techniques that explain H? theory maintains a link between the structure of the original model and the reduced order model. In other words, the state variables and the coefficients of the ROM have no connection to the state variables of the original model. In contrast the algorithm developed in this research derived from the Bond Graph, maintains a subset of original state variables in ROM and maintains structural significance in the state variable coefficients.