Tools for Modelling and Identification with Bond Graphs and Genetic Programming

[摘要] The contributions of this work include genetic programming grammars forbond graph modelling and for direct symbolic regression of sets of differentialequations; a bond graph modelling library suitable for programmatic use; asymbolic algebra library specialized to this use and capable of, among otherthings, breaking algebraic loops in equation sets extracted from linear bondgraph models. Several non-linear multi-body mechanics examples are pre-sented, showing that the bond graph modelling library exhibits well-behavedsimulation results. Symbolic equations in a reduced form are produced au-tomatically from bond graph models. The genetic programming system istested against a static non-linear function identification problem using type-less symbolic regression. The direct symbolic regression grammar is shownto have a non-deceptive fitness landscape: perturbations of an exact pro-gram have decreasing fitness with increasing distance from the ideal. Theplanned integration of bond graphs with genetic programming for use as asystem identification technique was not successfully completed. A catego-rized overview of other modelling and identification techniques is included ascontext for the choice of bond graphs and genetic programming.

[发布机构] University of Waterloo

[关键词] Mechanical Engineering