Dynamic modeling and identification of a slider-crank mechanism

In this paper, Hamiltonʹs principle, Lagrange multiplier, geometric constraints and partitioning method are employed to derive the dynamic equations of a slider-crank mechanism driven by a servomotor. The formulation is expressed by only one independent variable and considers the effects of mass, external force and motor electric inputs. Comparing the dynamic responses between the experimental results and numerical simulations, the dynamic modeling gives a wonderful interpretation of a slider-crank mechanism. The parameters of many industrial machines are difficult to obtain if these machines cannot be taken apart. In this paper, a new identification method based on the real-coded genetic algorithm (RGA) is presented to identify the parameters of a slider-crank mechanism. The method promotes the calculation efficiency very much, and is calculated by the real-code without the operations of encoding and decoding. The results of numerical simulations and the experiments prove that the identification method is feasible. Finally, the experimental results by the RGA and the recursive least squares (RLS) are also compared.