Comparison between mathematical modeling and experimental identification of a spatial slider–crank mechanism

In this paper, Hamilton’s principle, Lagrange multiplier, geometric constraints, partitioning method and Baumgarte stabilization method (BSM) are employed to derive the dynamic equations of a spatial slider–crank mechanism that is driven by a servomotor. The formulation considers the effects of links masses, external forces and motor electric inputs. Comparing dynamic responses between the experimental results and numerical simulations, dynamic modeling gives a wonderful interpretation for the spatial slider–crank mechanism. In this paper, a new identification method based on real-coded genetic algorithm (RGA) is presented to identify the parameters of a spatial slider–crank mechanism. The method promotes the calculation efficiency very much, and is calculated by real-code without the operations of encoding and decoding. The results of numerical simulations and experimental results prove that the identification method is feasible. The contributions of this paper are that the comparison of mathematical modeling and identification between numerical simulations and experimental results are all realized.