System Identification of Dual-rate Hammerstein CARARMA Systems by Multi-innovation Improved Differential Evolution Algorithm

This study focuses on the parameter estimation of the dual-rate Hammerstein-controlled autoregressive moving average (CARARMA) system. Because dual-rate systems have variables that cannot be directly measured, we use the auxiliary model to obtain a transformed model which can be directly identified. Aiming at the dual-rate Hammerstein CARARMA system, a multi-innovation improved differential evolution identification method is proposed, and its effectiveness is experimentally verified. By comparing with the improved differential evolution algorithm, it can be found that the proposed algorithm can give more accurate parameter estimates.