Equivalent Viscous Damping Estimation for Double-Sided Linear Switched Reluctance Motors Using Data-Driven Method

Accurately obtaining viscous damping is crucial for double-sided linear switched reluctance motors (DLSRMs). However, due to the nonlinear and time-varying characteristics of DLSRMs, obtaining accurate viscous damping is challenging. In this paper, a data-driven method is proposed for accurately estimating the equivalent viscous damping of DLSRMs. First, a dynamic model of DLSRMs is established, resulting in a transfer function model that incorporates the equivalent viscous damping to be estimated. Three discretization methods are applied to obtain the parameters of the discrete model, and then the relationships between these parameters and the equivalent damping coefficient are derived. Moreover, the recursive least squares algorithm is used to identify the parameters of the discrete model, enabling precise estimation of the equivalent viscous damping. Finally, experiments are performed to estimate the equivalent viscous damping, and the effectiveness of the proposed approach is verified.