Abstract
An algorithm based on mixed signals is proposed, to solve the issues of low accuracy of identification algorithm, immeasurable intermediate variables of fractional order Hammerstein model, and how to determine the magnitude of fractional order. In this paper, a special mixed input signal is designed to separate the nonlinear and linear parts of the fractional order Hammerstein model so that each part can be identified independently. The nonlinear part is fitted by the neural fuzzy network model, which avoids the limitation of polynomial fitting and broadens the application range of nonlinear models. In addition, the multi-innovation Levenberg-Marquardt (MILM) algorithm and auxiliary recursive least square algorithm are innovatively integrated into the parameter identification algorithm of the fractional order Hammerstein model to obtain more accurate identification results. A simulation example is given to verify the accuracy and effectiveness of the proposed method.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Additional information
Funding
Notes on contributors
Mengqi Sun
Mengqi Sun was born in 1998. She is a master degree candidate in Control Engineering at Dalian University of Technology. Her major is fractional order system identification.
Hongwei Wang
Hongwei Wang was born in 1969, doctor of engineering, professor. Graduated from Harbin Industry in 1999 University of Automatic Control Department. Now working in the School of Control Science and Engineering, Dalian University of Technology. Has published 110 SCI, EI and other search papers, and 2 invention patents. Host and participate in 5 national and local projects. Research direction: network control system, multi-sampling rate system control, switching system control.
Qian Zhang
Qian Zhang was born in 1995. She is a PhD candidate in the School of Electrical Engineering, Xinjiang University. Her research direction: Multi-model identification of fractional nonlinear systems.