Parameter identification of DC motor model based on improved dynamic forgetting factor recursive least squares method

In this paper, a DC motor parameter identification algorithm based on improved dynamic forgetting factor is proposed to replace the recursive least squares (RLS) method to determine the DC motor parameters. The effect of the improved dynamic FFRLS algorithm is studied, based on the difference between the theoretical and actual outputs of the model. The forgetting factor adjustment function is constructed. Using the improved dynamic FFRLS algorithm, the initial fluctuation segment is removed and the algorithm starts directly from the stable segment. The improved FFRLS algorithm is used to identify the DC motor parameters, and the moment of inertia and viscous friction coefficient of the DC motor are identified. The simulation results of recursive least squares(RLS) algorithm, FFRLS algorithm and the improved FFRLS algorithm show that the improved algorithm has the ability of dynamic fast convergence while maintaining the steady-state anti-interference ability, which is better than FFRLS and RLS algorithm.