Comparison between PSO and OLS for NARX parameter estimation of a DC motor

Recent works suggest that the Particle Swarm Optimization (PSO) algorithm is a highly-efficient optimization technique for structure selection of NARMAX and its derivative models. This research extends those findings by proposing PSO for parameter estimation of a Nonlinear Auto-Regressive with Exogenous (NARX) model for a Direct Current (DC) motor. The proposed method was compared to the established Orthogonal Least Squares (OLS) method. The findings indicate that PSO was comparable to OLS in solving the Least Squares (LS) parameter estimation problem posed in the NARX model.