非线性系统
控制理论(社会学)
自适应控制
控制(管理)
计算机科学
物理
人工智能
量子力学
作者
Zhen Gao,Yongduan Song,Marios M. Polycarpou
标识
DOI:10.1109/tcyb.2024.3438288
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI