Search publications

Reset filters Search by keyword

No publications found.

 

A type-3 fuzzy synchronization system subjected to hysteresis quantizer inputs and unknown dynamics: Applicable to financial and physical chaotic systems

Authors: Tian MMohammadzadeh ATaghavifar HSakthivel RZhang C


Affiliations

1 School of Management, Guangdong University of Science and Technology, Dongguan 523083, China. Electronic address: tianmv168@gmail.com.
2 Faculty of Engineering, Department of Electrical and Electronics Engineering, Sakarya University, Sakarya, Türkiye; Multidisciplinary Center for Infrastructure Engineering, Shenyang University of Technology, Shenyang, China. Electronic address: ardashir@sakarya.edu.tr.
3 Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, Canada. Electronic address: hamid.taghavifar@concordia.ca.
4 Department of Applied Mathematics, Bharathiar University, Coimbatore, India. Electronic address: krsakthivel0209@gmail.com.
5 Multidisciplinary Center for Infrastructure Engineering, Shenyang University of Technology, Shenyang, China. Electronic address: zhangchunwei@sut.edu.cn.

Description

In this paper, a new control approach is proposed for the synchronization and stabilization of a class of chaotic systems with unknown dynamics and input nonlinearities. Type-3 fuzzy logic systems (T3-FLSs) are developed to adaptively model the dynamics of both master and slave systems in real time. The input is affected by sector-bounded hysteresis and quantization, and these challenges are explicitly addressed in the control design. Unlike conventional methods, the proposed strategy does not require prior knowledge of the system equations or the derivatives of system signals. The adaptation laws for the T3-FLS parameters and estimation errors are rigorously derived using stability and robustness analysis, ensuring smooth control signals without chattering. Extensive simulations and real-time examinations demonstrate that the method achieves accurate synchronization even under severe uncertainties, high levels of random noise, and non-identical chaotic systems. Comparative results confirm the superiority of the proposed approach over existing fuzzy control methods.


Keywords: Adaptive controlChaotic systemsIdentificationNon-identical synchronizationSector-bounded quantizerType-3 fuzzy logic


Links

PubMed: https://pubmed.ncbi.nlm.nih.gov/41381323/

DOI: 10.1016/j.isatra.2025.12.007