Adaptive Security Control Using Output Only for Quantized Nonlinear Systems Under Irregularly Intermittent DoS Attacks

Quantized signal-driven control for nonlinear systems is of special interest in practice. However, it is nontrivial in the presence of mismatched uncertainties and intermittent denial of service (DoS) attacks. The underlying problem becomes even more complicated when both the input and output signals are attacked, rendering the state variables and the input signal inaccessible or unavailable for the control design. Only the quantized (and thus nondifferentiable) output signal is available in the absence of attack, making regular backstepping design inapplicable. This article introduces a novel adaptive output feedback control method to tackle the aforementioned challenges. First, we design a gain-switched quantized observer to estimate the unmeasurable state variables. Second, by employing a first-order dynamic filtering technique, we circumvent the nondifferentiability issue of virtual controller arising from the signal quantization. Third, we establish design conditions for the controller parameters. Fourth, we utilize a more comprehensive sector quantizer and develop adaptive estimators to deal with the unknown quantization parameters. Finally, we demonstrate that with the proposed control method, all the closed-loop signals are semiglobally uniformly ultimately bounded (SUUB), and the regulation error can be made small enough by appropriately tuning the design parameters. Numerical simulations confirm the efficacy of the proposed approach.

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Publication Details

Journal
IEEE Transactions on Cybernetics
Published
2024-08-26
DOI
https://doi.org/10.1109/tcyb.2024.3438288
Citations
5
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
1.74

Funders

Controls
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article

Adaptive Security Control Using Output Only for Quantized Nonlinear Systems Under Irregularly Intermittent DoS Attacks

Yongduan Song, Marios M. Polycarpou, Zhen Gao
5 citations
IEEE Transactions on Cybernetics
Network Security and Intrusion Detection
1.74
article

Adaptive Security Control Using Output Only for Quantized Nonlinear Systems Under Irregularly Intermittent DoS Attacks

Yongduan Song, Marios M. Polycarpou, Zhen Gao
article en
5 citations

Abstract

Quantized signal-driven control for nonlinear systems is of special interest in practice. However, it is nontrivial in the presence of mismatched uncertainties and intermittent denial of service (DoS) attacks. The underlying problem becomes even more complicated when both the input and output signals are attacked, rendering the state variables and the input signal inaccessible or unavailable for the control design. Only the quantized (and thus nondifferentiable) output signal is available in the absence of attack, making regular backstepping design inapplicable. This article introduces a novel adaptive output feedback control method to tackle the aforementioned challenges. First, we design a gain-switched quantized observer to estimate the unmeasurable state variables. Second, by employing a first-order dynamic filtering technique, we circumvent the nondifferentiability issue of virtual controller arising from the signal quantization. Third, we establish design conditions for the controller parameters. Fourth, we utilize a more comprehensive sector quantizer and develop adaptive estimators to deal with the unknown quantization parameters. Finally, we demonstrate that with the proposed control method, all the closed-loop signals are semiglobally uniformly ultimately bounded (SUUB), and the regulation error can be made small enough by appropriately tuning the design parameters. Numerical simulations confirm the efficacy of the proposed approach.

IEEE Transactions on CyberneticsVol. 54(11)
Chongqing University (CN), University of Cyprus (CY)
National Natural Science Foundation of China, Research and Innovation Foundation, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 14%
Network Security and Intrusion Detection
1.74
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