Parameter estimation for block-oriented nonlinear systems using the key term separation

This article considers the parameter estimation problems of block-oriented nonlinear systems. By using the key term separation, the system output is represented as a linear combination of unknown parameters. We give a key term separation auxiliary model gradient-based iterative (KT-AM-GI) identification algorithm and propose a key term separation auxiliary model three-stage gradient-based iterative (KT-AM-3S-GI) identification algorithm by using the hierarchical identification principle. Meanwhile, the multiinnovation theory is used to derived the key term separation auxiliary model three-stage multiinnovation gradient-based iterative (KT-AM-3S-MIGI) algorithm. The analysis shows that compared with the KT-AM-GI algorithm, the KT-AM-3S-GI algorithm can improve the parameter estimation accuracy and reduce the computational burden. In addition, the KT-AM-3S-MIGI can give more accurate parameter estimates than the KT-AM-3S-GI algorithm and can track time-varying parameters based on the dynamical window data. This work provides a reference for improving the identification performance of multiinput nonlinear output-error systems or multivariable nonlinear systems. The simulation results confirm the effectiveness of the proposed algorithm.