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

• Published in: International journal of robust and nonlinear control Vol. 30; no. 9; pp. 3727 - 3752
• Main Authors: Ji, Yan, Zhang, Chen, Kang, Zhen, Yu, Tao
• Format: Journal Article
• Language: English
• Published: Bognor Regis Wiley Subscription Services, Inc 01.06.2020
• Subjects: Algorithms; Computer simulation; hierarchical identification; Iterative methods; key term separation; Mathematical models; Monte Carlo simulation; multiinnovation theory; nonlinear system; Nonlinear systems; Parameter estimation; Separation; System effectiveness
• ISSN: 1049-8923; 1099-1239
• DOI: 10.1002/rnc.4961

Summary 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.