Iterative identification methods for a class of bilinear systems by using the particle filtering technique

Summary This article mainly studies the iterative parameter estimation problems of a class of nonlinear systems. Based on the auxiliary model identification idea, this article utilizes the estimated parameters to construct an auxiliary model, and uses its outputs to replace the unknown noise‐free process outputs, and develops an auxiliary model least squares‐based iterative (AM‐LSI) identification algorithm. For further improving the parameter estimation accuracy, we use a particle filter to estimate the unknown noise‐free process outputs, and derive a particle filtering least squares‐based iterative (PF‐LSI) identification algorithm. During each iteration, the AM‐LSI and PF‐LSI algorithms can make full use of the measured input–output data. The simulation results indicate that the proposed algorithms are effective for identifying the nonlinear systems, and can generate more accurate parameter estimates than the auxiliary model‐based recursive least squares algorithm.