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Multibody Model Simplification by Parameter Reduction


 Model selection methods are used in different scientific contexts to represent a characteristic data set in terms of a reduced number of parameters. Apparently, these methods have not found their way in the multibody systems dynamics literature. Multibody models can be considered parametric models in terms of their dynamic parameters and model selection techniques can then be used to express these models in terms of a reduced number of parameters. These parameter-reduced models are expected to have a computational complexity smaller than the original one, and still preserve the desired level of accuracy. They are also known to be good candidates for parameter estimation purposes. In this work, simulations of the actual model are used to define a data set representative of the standard working conditions of the system. A parameter-reduced model is chosen and its parameter values estimated so that they minimize a prediction error on these data. To that end, model selection heuristics as well as normalized error measures are proposed. Using this methodology two multibody systems with very different characteristic mobility are analyzed. Very important reductions in the number of parameters and in the computational cost are obtained without compromising too much on the reduced model accuracy. As an aside result, a generalization of the base parameter concept to the context of parameter-reduced models is proposed.