Iterative parameter estimation methods for dual‐rate sampled‐data bilinear systems by means of the data filtering technique

Abstract

This paper considers the iterative parameter estimation for a dual-rate sampled-data bilinear system with autoregressive moving average noise. Through combining the auxiliary model identification idea with the data filtering technique, this paper derives two filtering auxiliary model gradient-based iterative algorithms by using two different filters. The key is to construct an auxiliary model for predicting the unavailable outputs, and to transform the dual-rate bilinear system identification model into two sub-identification models. Finally, an auxiliary model gradient-based iterative (AM-GI) algorithm is presented for comparison. The simulation results indicate that the proposed algorithms are effective for identifying the dual-rate sampled-data bilinear systems, and can generate more accurate parameter estimates and have a higher computational efficiency than the AM-GI algorithm.