Full Text:   <159>

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CLC number: TP273

On-line Access: 2024-02-23

Received: 2023-09-13

Revision Accepted: 2024-02-23

Crosschecked: 2023-12-03

Cited: 0

Clicked: 281

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Mingguang ZHANG

https://orcid.org/0009-0005-0205-6563

Feng LI

https://orcid.org/0000-0001-9445-1627

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Frontiers of Information Technology & Electronic Engineering  2024 Vol.25 No.2 P.260-271

http://doi.org/10.1631/FITEE.2300620


Estimation of Hammerstein nonlinear systems with noises using filtering and recursive approaches for industrial control


Author(s):  Mingguang ZHANG, Feng LI, Yang YU, Qingfeng CAO

Affiliation(s):  School of Electrical & Information Engineering, Jiangsu University of Technology, Changzhou 213001, China; more

Corresponding email(s):   lifeng@jsut.edu.cn

Key Words:  Hammerstein nonlinear systems, Neural fuzzy network, Data filtering, Hybrid signals, Industrial control


Mingguang ZHANG, Feng LI, Yang YU, Qingfeng CAO. Estimation of Hammerstein nonlinear systems with noises using filtering and recursive approaches for industrial control[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(2): 260-271.

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Abstract: 
This paper discusses a strategy for estimating hammerstein nonlinear systems in the presence of measurement noises for industrial control by applying filtering and recursive approaches. The proposed hammerstein nonlinear systems are made up of a neural fuzzy network (NFN) and a linear state–‍space model. The estimation of parameters for Hammerstein systems can be achieved by employing hybrid signals, which consist of step signals and random signals. First, based on the characteristic that step signals do not excite static nonlinear systems, that is, the intermediate variable of the Hammerstein system is a step signal with different amplitudes from the input, the unknown intermediate variables can be replaced by inputs, solving the problem of unmeasurable intermediate variable information. In the presence of step signals, the parameters of the state‍–‍space model are estimated using the recursive extended least squares (RELS) algorithm. Moreover, to effectively deal with the interference of measurement noises, a data filtering technique is introduced, and the filtering-based RELS is formulated for estimating the NFN by employing random signals. Finally, according to the structure of the Hammerstein system, the control system is designed by eliminating the nonlinear block so that the generated system is approximately equivalent to a linear system, and it can then be easily controlled by applying a linear controller. The effectiveness and feasibility of the developed identification and control strategy are demonstrated using two industrial simulation cases.

基于滤波和递推的Hammerstein非线性系统估计与控制

张明光1,李峰1,俞洋1,曹晴峰2
1江苏理工学院电气信息工程学院,中国常州,213001
2扬州大学电气与能源动力工程学院,中国扬州,225127
摘要:本文提出一种基于滤波和递推的含测量噪声的Hammerstein系统参数估计与工业控制方法。Hammerstein非线性系统由神经模糊模型和线性状态空间模型组成,并利用由阶跃信号和随机信号组成的混合信号估计Hammerstein系统参数。首先,利用阶跃信号不激发静态非线性系统的特性,即Hammerstein系统的中间变量与输入具有不同幅值的阶跃信号,从而未知的中间变量可以利用输入替代,解决了中间变量信息不可测量问题。因此,基于设计的阶跃信号,利用递推增广最小二乘(RELS)算法估计状态空间模型参数。其次,为了有效处理测量噪声的干扰,引入数据滤波技术,并利用滤波RELS算法和聚类算法估计神经模糊模型参数。最后,利用Hammerstein系统的特殊结构,将非线性系统控制简化为线性系统控制,从而利用线性控制器进行控制。通过两个工业仿真案例验证了所提方法和控制策略的有效性和可行性。

关键词:Hammerstein非线性系统;神经模糊网络;数据滤波;混合信号;工业控制

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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