Multivariable Adaptive Super-Twisting Sliding Mode Resilient Control for Uncertain Nonlinear CPSs Against Actuator and Sensor Attacks

IF 6.4 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Automation Science and Engineering Pub Date : 2024-11-19 DOI:10.1109/TASE.2024.3496755
Yannan Bi;Fei Wang;Pansheng Ding;Tong Wang;Jianbin Qiu
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Abstract

This paper proposes an adaptive super-twisting sliding mode resilient control method for uncertain nonlinear cyber-physical systems (CPSs) subject to actuator and sensor attacks. Two super-twisting algorithms with adaptive gains are designed based on the compromised system states to counteract the effect of cyber-attacks to ensure the system states converge to a small region near zero. Furthermore, the gain overestimation problem of the sliding mode resilient controller is also discussed. Finally, a formal analysis of the closed-loop system is derived using Lyapunov function techniques. Simulation results on a two-link manipulator system validate the effectiveness of the proposed resilient control strategy. Note to Practitioners—The motivation of this paper is to address the secure issues of the nonlinear cyber-physical systems, including applications in robotic manipulators, vehicle systems, and power systems. Since some devices of CPSs, such as sensor and controller components, communicate via networked channels in practice, the original system states and control signals of CPSs may be tampered by cyber-attacks, leading to unavailable system states and severe uncertainties with unknown bounds. Although the super-twisting sliding mode control (ST-SMC) method is an effective tool for dealing with uncertainties, the control gains may be overestimated due to the intermittent nature of cyber attacks, resulting in severe chattering caused by the discontinuous term of the ST-SMC method. Furthermore, it is extremely challenging when only compromised system states are available for the controller, potentially generating false commands and destroying the entire system. Therefore, based on Lyapunov theory, a novel multivariable adaptive sliding mode resilient control method with two adaptive super-twisting algorithms is proposed to mitigate the effects caused by cyber-attacks. The effectiveness of the proposed method is validated using a two-link manipulator. Note that the proposed method has the potential for broader engineering applications in real-world scenarios.
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针对不确定非线性 CPS 的多变量自适应超扭曲滑模弹性控制,对抗致动器和传感器攻击
针对受致动器和传感器攻击的不确定非线性网络物理系统(CPSs),提出了一种自适应超扭滑模弹性控制方法。设计了两种具有自适应增益的超扭曲算法,以抵消网络攻击的影响,确保系统状态收敛到接近零的小区域。此外,还讨论了滑模弹性控制器的增益过估计问题。最后,利用李雅普诺夫函数技术推导了闭环系统的形式化分析。两连杆机械臂系统的仿真结果验证了所提弹性控制策略的有效性。从业人员注意:本文的动机是解决非线性网络物理系统的安全问题,包括在机器人操纵器、车辆系统和电力系统中的应用。由于cps的一些设备,如传感器和控制器组件,在实际中是通过网络通道进行通信的,因此cps原有的系统状态和控制信号可能会被网络攻击所篡改,导致系统状态不可用,产生严重的未知界不确定性。虽然超扭转滑模控制(ST-SMC)方法是处理不确定性的有效工具,但由于网络攻击的间歇性,控制增益可能会被高估,导致ST-SMC方法的不连续项导致严重的抖振。此外,当控制器只有受损的系统状态可用时,这是极具挑战性的,可能会生成错误的命令并破坏整个系统。为此,基于李雅普诺夫理论,提出了一种采用两种自适应超扭转算法的多变量自适应滑模弹性控制方法,以减轻网络攻击带来的影响。通过一个双连杆机械手验证了所提方法的有效性。请注意,所提出的方法在实际场景中具有更广泛的工程应用潜力。
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来源期刊
IEEE Transactions on Automation Science and Engineering
IEEE Transactions on Automation Science and Engineering 工程技术-自动化与控制系统
CiteScore
12.50
自引率
14.30%
发文量
404
审稿时长
3.0 months
期刊介绍: The IEEE Transactions on Automation Science and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. T-ASE welcomes results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, security, service, supply chains, and transportation. T-ASE addresses a research community willing to integrate knowledge across disciplines and industries. For this purpose, each paper includes a Note to Practitioners that summarizes how its results can be applied or how they might be extended to apply in practice.
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