Work

Weighted multi‐innovation parameter estimation for a time‐varying Volterra–Hammerstein system with colored noise

Year: 2024

Type: article

Abstract: This article considers the parameter estimation problems of a time‐varying Volterra–Hammerstein (V–H) system with colored noise. We derive a weighted multi‐innovation forgetting factor gradient algorithm by applying the multi‐innovation identification with the weight matrix to adjust the weights of the innovation vector. In order to simplify the identification algorithm and improve the parameter estimation accuracy, the original V–H system with colored noise is separated into two sub‐systems by the hierarchical identification theory. A weighted hierarchical multi‐innovation forgetting factor gradient algorithm is presented for two sub‐systems. Numerical simulation examples test the effectiveness of the proposed algorithms.

Source: Optimal Control Applications and Methods

DOI: 10.1002/oca.3210

Authors: Yanshuai Zhao, Yan Ji

Institution: Qingdao University of Science and Technology

Language: English

Topic: Control Systems and Identification

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: bronze

Funders: National Natural Science Foundation of China, Natural Science Foundation of Shandong Province

Awards: G3348564771, G5138572121