Adaptive Switching Control for Nonlinear Uncertain Systems With Sensor Faults and Quantization

In this article, global adaptive quantized control problem of a class of nonlinear uncertain systems with sensor faults is addressed, and a novel non-identification adaptive control method is proposed based on a switching mechanism. One advantage of the proposed method is that continuous multiplicative sensor faults are tolerated and handled in a generalized Lyapunov matrix inequality; another is its lower computational complexity compared to the traditional adaptive backstepping method (avoiding the ‘differential explosion’ problem). In addition, a new hysteresis logarithmic-type quantizer is developed to prevent chattering. The quantization error is dealt with by using sector bounded property, where the compensation of multiplicative deviation in quantization is similar to that of multiplicative sensor faults, while additive bias is treated as a disturbance (quantization dead-zone size determines the magnitude of the disturbance). Especially, the relationship between quantization dead-zone and control accuracy is clearly characterized. Also, it is shown that the proposed control method is robust to mismatched disturbances, and the solutions of the closed-loop system can be guaranteed to converge to the arbitrarily small neighborhood of the origin as long as the dead-zone of quantizer and mismatched disturbances are sufficiently small.

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Publication Details

Journal
IEEE Transactions on Circuits and Systems I Regular Papers
Published
2025-02-05
DOI
https://doi.org/10.1109/tcsi.2025.3537719
Citations
5
Primary Topic
Advanced Control Systems Optimization
Type
article
Field-Weighted Citation Impact
6.07

Funders

Controls
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article

Adaptive Switching Control for Nonlinear Uncertain Systems With Sensor Faults and Quantization

Guozeng Cui, Xianglei Jia, Shengyuan Xu
5 citations
IEEE Transactions on Circuits and Systems I Regular Papers
Advanced Control Systems Optimization
6.07
article

Adaptive Switching Control for Nonlinear Uncertain Systems With Sensor Faults and Quantization

Guozeng Cui, Xianglei Jia, Shengyuan Xu
article en
5 citations

Abstract

In this article, global adaptive quantized control problem of a class of nonlinear uncertain systems with sensor faults is addressed, and a novel non-identification adaptive control method is proposed based on a switching mechanism. One advantage of the proposed method is that continuous multiplicative sensor faults are tolerated and handled in a generalized Lyapunov matrix inequality; another is its lower computational complexity compared to the traditional adaptive backstepping method (avoiding the ‘differential explosion’ problem). In addition, a new hysteresis logarithmic-type quantizer is developed to prevent chattering. The quantization error is dealt with by using sector bounded property, where the compensation of multiplicative deviation in quantization is similar to that of multiplicative sensor faults, while additive bias is treated as a disturbance (quantization dead-zone size determines the magnitude of the disturbance). Especially, the relationship between quantization dead-zone and control accuracy is clearly characterized. Also, it is shown that the proposed control method is robust to mismatched disturbances, and the solutions of the closed-loop system can be guaranteed to converge to the arbitrarily small neighborhood of the origin as long as the dead-zone of quantizer and mismatched disturbances are sufficiently small.

IEEE Transactions on Circuits and Systems I Regular PapersVol. 72(6)
Suzhou University of Science and Technology (CN), Nanjing University of Science and Technology (CN), Hangzhou Dianzi University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province
Climate action
Openalex Percentile: Top 4%
Advanced Control Systems Optimization
6.07
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Adaptive Switching Control for Nonlinear Uncertain Systems With Sensor Faults and Quantization — Guozeng Cui, Xianglei Jia, et al. · IEEE Transactions on Circuits and Systems I Regular Papers (2025) | TGRS Research Map | TGRS