A Generalized Type-2 Fuzzy-Based Analog Memristive Controller

Type-1 fuzzy PID controllers are widely used in industrial control systems due to their well-established theoretical foundation, simplicity of structure, and ease of operation. However, as control systems become increasingly complex and demands for higher control performance intensify, the limitations of Type-1 fuzzy controllers become more apparent. Additionally, the difficulty in tuning PID parameters and the inability to adjust these parameters online as the controlled system changes further constrain the effectiveness of traditional PID controllers. To address these challenges, this paper introduces a generalized Type-2 fuzzy logic control approach. The generalized Type-2 fuzzy controller extends the capabilities of interval Type-2 fuzzy controllers, offering enhanced ability to handle higher-order uncertainties. Moreover, the integration of memristor devices enables online adaptive tuning of controller parameters. By combining the generalized Type-2 fuzzy logic with memristor-based PID control, a novel memristor-based controller was designed, which allows for real-time adaptive adjustment of the controller parameters. Theoretical analysis is followed by simulations and experimental validation. The results demonstrate that the proposed memristor-based controller with generalized Type-2 fuzzy logic exhibits superior robustness. Specifically, when evaluated using the Integral of Time-weighted Absolute Error (ITAE) performance index, the controller achieves improvements of 65.9% and 40.2% compared to traditional PID and Type-1 fuzzy PID controllers, respectively. These findings confirm the excellent tracking performance and robustness of the proposed control system.

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Publication Details

Journal
Electronics
Published
2025-03-17
DOI
https://doi.org/10.3390/electronics14061178
Citations
4
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
2.92
Controls
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article

A Generalized Type-2 Fuzzy-Based Analog Memristive Controller

Yao Mao, Xing‐Long Chen, Feng Teng, Chao Zhang et al.
4 citations
Electronics
Advanced Memory and Neural Computing
2.92
article

A Generalized Type-2 Fuzzy-Based Analog Memristive Controller

Yao Mao, Xing‐Long Chen, Feng Teng, Chao Zhang, Shuwei Deng
article en
4 citations

Abstract

Type-1 fuzzy PID controllers are widely used in industrial control systems due to their well-established theoretical foundation, simplicity of structure, and ease of operation. However, as control systems become increasingly complex and demands for higher control performance intensify, the limitations of Type-1 fuzzy controllers become more apparent. Additionally, the difficulty in tuning PID parameters and the inability to adjust these parameters online as the controlled system changes further constrain the effectiveness of traditional PID controllers. To address these challenges, this paper introduces a generalized Type-2 fuzzy logic control approach. The generalized Type-2 fuzzy controller extends the capabilities of interval Type-2 fuzzy controllers, offering enhanced ability to handle higher-order uncertainties. Moreover, the integration of memristor devices enables online adaptive tuning of controller parameters. By combining the generalized Type-2 fuzzy logic with memristor-based PID control, a novel memristor-based controller was designed, which allows for real-time adaptive adjustment of the controller parameters. Theoretical analysis is followed by simulations and experimental validation. The results demonstrate that the proposed memristor-based controller with generalized Type-2 fuzzy logic exhibits superior robustness. Specifically, when evaluated using the Integral of Time-weighted Absolute Error (ITAE) performance index, the controller achieves improvements of 65.9% and 40.2% compared to traditional PID and Type-1 fuzzy PID controllers, respectively. These findings confirm the excellent tracking performance and robustness of the proposed control system.

ElectronicsVol. 14(6)
Chinese Academy of Sciences (CN), Institute of Optics and Electronics, Chinese Academy of Sciences (CN), University of Chinese Academy of Sciences (CN)
Openalex Percentile: Top 10%
Advanced Memory and Neural Computing
2.92
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