Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6952738 | Journal of the Franklin Institute | 2018 | 16 Pages |
Abstract
Mathematical models are basic for designing controller and system identification is the theory and methods for establishing the mathematical models of practical systems. This paper considers the parameter identification for Hammerstein controlled autoregressive systems. Using the key term separation technique to express the system output as a linear combination of the system parameters, the system is decomposed into several subsystems with fewer variables, and then a hierarchical least squares (HLS) algorithm is developed for estimating all parameters involving in the subsystems. The HLS algorithm requires less computation than the recursive least squares algorithm. The computational efficiency comparison and simulation results both confirm the effectiveness of the proposed algorithms.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Feng Ding, Huibo Chen, Ling Xu, Jiyang Dai, Qishen Li, Tasawar Hayat,