Identification of a Robotic Arm Using Optimization Methods for Model Estimation

The system identification procedure is a powerful and flexible tool for the modeling of dynamic systems. This paper implements the theory of parametric identification in order to estimate a valid model of a flexible robotic arm. For this purpose experimental data is used for the estimation of ARMAX SISO models. A two-stages identification procedure (non-parametric & parametric) provides an insight about the system under identification. In the first stage, known signal analysis methods are applied (correlation-spectral analysis) for the estimation of frequencies and frequency response, and in the second stage, the estimation of ARMAX models is performed in order to fit a transfer function model to collected input-output data set. For the estimation of model's coefficients, use of Evolutionary Algorithms is implemented.