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针对存在网络攻击的基于驻留概率的切换系统的滑模控制

Sliding-Mode Control for Sojourn-Probability-Based Switching Systems With Cyber-Attacks.

作者信息

Cheng Jun, Zhang Qiongwen, Yan Huaicheng, Zhang Dan, Park Ju H

出版信息

IEEE Trans Cybern. 2025 Jul;55(7):3099-3107. doi: 10.1109/TCYB.2025.3565806.

DOI:10.1109/TCYB.2025.3565806
PMID:40366820
Abstract

This study investigates an asynchronous sliding-mode control (SMC) strategy tailored for interval type-2 (IT2) fuzzy switching systems, specifically addressing challenges posed by cyber-attacks. Distinct from existing stochastic switching strategies, a novel duration-time-based switching rule is proposed that integrates both sojourn probability and mode duration, significantly reducing computational complexity and aligning more closely with practical requirements. To mitigate mode-switching-induced chattering and enhance robustness against uncertainties and disturbances, an innovative fuzzy SMC law with a learning mechanism is developed. Notably, a recursive sliding-mode learning controller is introduced, replacing abrupt switching actions with iterative learning adjustments to progressively guide system states onto the sliding surface, thereby significantly improving control smoothness and reducing chattering. To effectively handle cyber-attacks disrupting mode transmission, a comprehensive mismatched model that dynamically synchronizes the modes of the system and the controller is introduced, offering improved resilience compared to traditional fixed mismatch approaches. Utilizing the proposed learning-based SMC and Lyapunov stability theory, sufficient conditions ensuring mean-square stability of the system are derived. Finally, the practical effectiveness and distinct superiority of the proposed methods are demonstrated through simulations using mass-spring-damper and tunnel diode circuit models.

摘要

本研究探讨了一种针对区间二型(IT2)模糊切换系统量身定制的异步滑模控制(SMC)策略,专门应对网络攻击带来的挑战。与现有的随机切换策略不同,提出了一种新颖的基于持续时间的切换规则,该规则整合了驻留概率和模式持续时间,显著降低了计算复杂度,并更紧密地符合实际需求。为了减轻模式切换引起的抖动,并增强对不确定性和干扰的鲁棒性,开发了一种具有学习机制的创新模糊SMC律。值得注意的是,引入了一种递归滑模学习控制器,用迭代学习调整取代突然的切换动作,以逐步将系统状态引导到滑模面上,从而显著提高控制平滑度并减少抖动。为了有效应对破坏模式传输的网络攻击,引入了一种全面的失配模型,该模型动态同步系统和控制器的模式,与传统的固定失配方法相比,具有更高的恢复能力。利用所提出的基于学习的SMC和李雅普诺夫稳定性理论,推导了确保系统均方稳定的充分条件。最后,通过使用质量 - 弹簧 - 阻尼器和隧道二极管电路模型进行仿真,证明了所提方法的实际有效性和明显优势。

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