基于生成对抗网络的目标检测黑盒迁移攻击算法

目标检测被广泛应用到自动驾驶、工业、医疗等各个领域. 利用目标检测算法解决不同领域中的关键任务逐渐成为主流. 然而基于深度学习的目标检测模型在对抗样本攻击下, 模型的鲁棒性存在严重不足, 通过加入微小扰动构造的对抗样本很容易使模型预测出错. 这极大地限制了目标检测模型在关键安全领域的应用. 在实际应用中的模型普遍是黑盒模型, 现有的针对目标检测模型的黑盒攻击相关研究不足, 存在鲁棒性评测不全面, 黑盒攻击成功率较低, 攻击消耗资源较高等问题. 针对上述问题, 提出基于生成对抗网络的目标检测黑盒攻击算法, 所提算法利用融合注意力机制的生成网络直接输出对抗扰动, 并使用替代模型的损失和所提的类别注意力损失共同优化生成网络参数, 可以支持定向攻击和消失攻击两种场景. 在Pascal VOC数据集和MS COCO数据集上的实验结果表明, 所提方法比目前攻击方法的黑盒迁移攻击成功率更高, 并且可以在不同数据集之间进行迁移攻击.     

FDI攻击下自触发预测控制安全策略研究

针对信息物理融合系统自触发模型预测控制在虚假数据注入(FDI)攻击下的安全控制问题,提出一种基于控制信息重构的安全控制策略。首先,在控制器端对连续控制信号进行离散采样,并对少数关键数据进行重点加密;其次,在网络端传输离散化后的控制信号样本,并在被控系统处基于预设模式对控制信号进行重构;然后,推导了在系统遭受虚假数据注入攻击之时,重点加密数据的选取条件,并在理论上严格证明了基于重点加密数据重构获得的控制信号能够保证控制系统的可行性与稳定性;最后,基于机器人系统进行了仿真实验,验证了算法的有效性。     

基于高阶观测器和干扰补偿控制的模型预测控制方法

针对状态不可测、外部干扰未知, 并且状态和输入受限的离散时间线性系统, 将高阶观测器、干扰补偿控制与标准模型预测控制(Model predictive control, MPC)相结合, 提出了一种新的MPC方法. 首先利用高阶观测器同步观测未知状态和干扰, 使得观测误差一致有界收敛;然后基于该干扰估计值设计新的干扰补偿控制方法, 并将该方法与基于状态估计的标准MPC相结合, 实现上述系统的优化控制. 所提出的MPC方法克服了利用现有MPC方法求解具有外部干扰和状态约束的优化控制问题时存在无可行解的局限, 能够保证系统状态在每一时刻都满足约束条件, 并且使系统的输出响应接近采用标准MPC方法控制线性标称系统时得到的输出响应. 最后, 将所提控制方法应用到船舶航向控制系统中, 仿真结果表明了所提方法的有效性和优越性.     

带有欺骗攻击的区间二型T-S模糊系统的 高效模型预测控制

研究欺骗攻击影响下的区间二型T-S模糊系统的高效模型预测控制(efficient model predictive control, EMPC)问题.考虑到数据从传感器节点传输到控制器节点受到欺骗攻击的影响,设计一种EMPC框架下基于状态观测器的输出反馈控制器.首先,针对能量有界的欺骗攻击,构建辅助优化问题,离线求解终端约束集以及满足系统$H_2$安全的固定反馈控制增益和观测器增益;其次,引入摄动量提供控制自由度,离线求解增广系统的不变集,通过最大化该不变集在原状态空间的投影得到初始可行域;然后,在线优化摄动量得到满意的控制律,确保系统在欺骗攻击影响下的安全性;最后,给出所提出的EMPC算法理论可行性和系统安全性的证明,并通过仿真实例验证所提出算法的有效性.     

约束Hammerstein系统非线性预测控制及在聚丙烯牌号切换中的仿真研究

对状态和输入受约束的Hammerstein系统, 提出一种新的可保证闭环指数稳定的非线性模型预测控制策略. 基于线性子系统镇定的最优控制律, 滚动预测非线性代数方程的解算误差, 继而在线优化计算满足约束的预测控制量. 进一步, 得到闭环系统指数稳定的解算误差上界. 从而闭环系统不仅满足约束而且对解算误差具有鲁棒性. 最后以工业聚丙烯牌号切换控制为例, 仿真验证本文算法的有效性.     

DoS攻击下网络化控制系统基于观测器的记忆型事件触发预测控制

本文研究了DoS攻击下网络化控制系统记忆型事件触发预测补偿控制问题. 首先, 由于网络带宽资源有限 和系统状态不完全可观测性, 引入了记忆型事件触发函数, 为观测器提供离散事件触发传输方案. 然后, 分析了网络 传输通道上发生的DoS攻击. 结合上述记忆型事件触发方案, 在控制节点设计一类新颖的预测控制算法, 节省网络 带宽资源并主动补偿DoS攻击. 同时, 建立了基于观测器的记忆型事件触发预测控制的闭环系统, 并且分析稳定性. 通过线性矩阵不等式(LMI)和Lyapunov稳定性理论, 建立了控制器、观测器和记忆型事件触发矩阵的联合设计方案, 并验证了该方案的可行性. 仿真结果表明, 该方案结合记忆型事件触发机制可以有效补偿DoS攻击, 节约网络带宽 资源.     

遥控水下机器人模型辨识及预测控制算法研究

针对遥控水下机器人（remotely operated vehicle, ROV）因非对称结构布局、重心和浮心位置变化等因素造成的运动状态耦合和模型非线性问题,文章提出了一种考虑ROV典型非线性特性的在线模型辨识方法,研究并推导了基于所辨识模型的多维预测控制（model predictive control, MPC）算法及其基于算子分割二次规划的快速优化求解方法,搭建了包含ROV非线性数学模型、模型参数辨识算法模块和MPC算法模块的算法仿真系统。通过在辨识模型中分别考虑非线性特性和不考虑非线性特性,改变ROV数学仿真模型的ROV浮心位置参数,对比仿真验证了本文模型辨识算法的有效性和先进性;在上述辨识模型的基础上,对比仿真分析了4种情况下ROV的姿态控制效果,即MPC算法依托的辨识模型是否考虑ROV典型非线性特性,以及基于直接求解MPC算法和本文所提出的基于快速优化近似求解的MPC算法,验证了本文所提控制算法的有效性。     

事件触发脉冲控制多智能体系统的安全一致

对多智能体系统在欺骗攻击下的均方有界一致性问题进行研究,并在固定时间脉冲控制的基础上结合事件触发控制,设计了一种具有触发时间上界的事件触发脉冲控制方法。利用李雅普诺夫稳定性理论、图论和线性矩阵不等式技巧,得到了多智能体系统的均方有界一致的充分条件,并验证了所提出的事件触发脉冲控制方法可以自动调节脉冲时间间隔,快速达到安全一致。最后,数值仿真结果进一步验证了理论研究结果的有效性。     

欺骗攻击下具备隐私保护的多智能体系统均值趋同控制

针对通信网络遭受欺骗攻击的离散时间多智能体系统,研究其均值趋同和隐私保护问题.首先,考虑链路信道存在窃听者的情形,提出一种基于状态分解思想的分布式网络节点值重构方法,以阻止系统初始信息的泄露.其次,针对所构建的欺骗攻击模型,利用重构后节点状态信息并结合现有的安全接受广播算法,提出一种适用于无向通信网络的多智能体系统均值趋同控制方法.理论分析表明,该方法能够有效保护节点初始状态信息的隐私,并能消除链路中欺骗攻击的影响,实现分布式系统中所有节点以初始值均值趋同.最后,通过数值仿真实验验证了该方法的有效性.     

基于事件触发的二阶多智能体领导跟随一致性

研究二阶多智能体系统在固定有向拓扑下的领导跟随一致性问题.为了节省网络和计算资源,给出一种基于事件触发控制的一致性算法.针对每个跟随者智能体,设计基于状态误差形式的触发函数,只有当状态误差满足一定条件,即触发函数取值为零时智能体才触发事件,同时更新并传递自身的采样信息,在两个相邻事件触发时刻之间,控制输入只受领导者控制信号的影响.利用模型变换、矩阵理论和Lyapunov稳定性理论给出多智能体系统达到领导跟随一致性的充分条件.仿真结果验证了理论方案的可行性和有效性.     

Event-triggered containment control for multi-agent systems under hybrid cyber attacks

This paper studies event-triggered containment control problem of multi-agent systems (MASs) under deception attacks and denial-of-service (DoS) attacks. First, to save limited network resources, an event-triggered mechanism is proposed for MASs under hybrid cyber attacks. Different from the existing event-triggered mechanisms, the negative influences of deception attacks and DoS attacks are considered in the proposed triggering function. The communication frequencies between agents are reduced. Then, based on the proposed event-triggered mechanism, a corresponding control protocol is proposed to ensure that the followers will converge to the convex hull formed by the leaders under deception attacks and DoS attacks. Compared with the previous researches about containment control, in addition to hybrid cyber attacks being considered, the nonlinear functions related to the states of the agents are applied to describe the deception attack signals in the MAS. By orthogonal transformation of deception attack signals, the containment control problem under deception attacks and DoS attacks is reformulated as a stability problem. Then, the sufficient conditions on containment control can be obtained. Finally, a set of simulation example is used to verify the effectiveness of the proposed method.     

高速列车精确停车的鲁棒自触发预测控制

列车精确停车作为列车自动运行(Automatic train operation, ATO)系统的一项核心功能, 对高速列车的安全和高效运行至关重要. 本文针对高速列车停车过程的特点, 考虑在避免控制输出频繁切换的前提下实现高精度的停车曲线跟踪, 提出了基于模型预测控制(Model predictive control, MPC)的精确停车算法. 针对列车停车过程中外部不确定性阻力干扰, 采用鲁棒模型预测控制方法, 提高对外部干扰的鲁棒性. 引入自触发控制策略, 以进一步减少控制输出的频繁切换, 提高停车过程的舒适度. 该方法不需要每个采样时间都求解线性约束二次规划问题, 降低了对系统采样和通信能力的要求, 提高了算法的实用性. 分析结果表明, 高速列车精确停车控制方法的稳定性和性能指标的次优性可以得到保证. 基于高速列车实际运行数据的仿真结果验证了算法的有效性.     

基于一种改进型逆凸不等式的混合攻击下多区域时滞电力系统的分布式控制

本文研究了混合攻击下多区域时滞电力系统的分布式协调控制问题。首先,考虑了一种基于混合攻击下（包括拒绝服务攻击和欺骗攻击）的多区域时滞电力系统,在拒绝服务攻击下,时滞电力系统的各个区域之间的通信将恶化为一种切换残余拓扑结构,它的时间特性建模成了模型依赖的平均停留时间的模型。在以负反馈控制为目标的欺骗攻击下,将其建模成了基于符号函数的模型。然后,为了在低惯性和混合攻击下获得时滞电力系统的弹性性能,采用了负荷频率控制与虚拟惯性控制相结合的弹性分布式策略,并将时滞电力系统的负荷频率控制问题转换为切换非线性切换系统控制问题。再通过采用合适的Lyapunov-Krasovskii泛函、改进型逆凸不等式、线性矩阵不等式技术以及切换系统方法,得到了多区域时滞电力系统的稳定性定理。并基于此定理,设计了负荷频率控制和虚拟惯性控制下的分布式控制增益。最后,建立仿真,针对三区域电力系统进行了仿真,仿真结果体现了本文所提出来的稳定性定理和设计的分布式控制增益的优势和有效性。     

Adaptive model predictive control for a class of constrained linear systems based on the comparison model

This paper proposes an adaptive model predictive control (MPC) algorithm for a class of constrained linear systems, which estimates system parameters on-line and produces the control input satisfying input/state constraints for possible parameter estimation errors. The key idea is to combine the robust MPC method based on the comparison model with an adaptive parameter estimation method suitable for MPC. To this end, first, a new parameter update method based on the moving horizon estimation is proposed, which allows to predict an estimation error bound over the prediction horizon. Second, an adaptive MPC algorithm is developed by combining the on-line parameter estimation with an MPC method based on the comparison model, suitably modified to cope with the time-varying case. This method guarantees feasibility and stability of the closed-loop system in the presence of state/input constraints. A numerical example is given to demonstrate its effectiveness.     

Adaptive control Lyapunov function based model predictive control for continuous nonlinear systems

A design of adaptive model predictive control (MPC) based on adaptive control Lyapunov function (aCLF) is proposed in this article for nonlinear continuous systems with part of its dynamics being unknown at the starting time. Specifically, to guarantee the convergence of the closed-loop system with online predictive model updating, a stability constraint is designed. It limits the aCLF of the system under the MPC to be less than that under an online updated auxiliary adaptive control. The auxiliary adaptive control which implements in a sampling-hold fashion can guarantee the convergence of the controlled system. The sufficient conditions that guarantee the states to be steered to a small region near the equilibrium by the proposed MPC are provided. The calculation of the proposed algorithm does not depend on the model mismatch at the starting time. And it does not require the Lyapunov function of the state of the real system always to be reduced at each time. These provide the potential to improve the performance of the closed-loop system. The effectiveness of the proposed method is illustrated through a chemical process example.     

Model predictive tracking control for a linear system under time‐varying input constraints

In this paper, we propose a tracking control law for a linear dynamical system under time‐varying input constraints. The proposed control law consists of a dual‐mode model predictive control (MPC) law and a target recalculation mechanism. As the terminal controller of the dual‐mode MPC, we propose a saturation‐level‐dependent gain‐scheduled feedback control law that ensures closed‐loop stability against arbitrary change of the position limit of the actuators. We also present conditions that guarantee feasibility and stability of the control algorithm under time‐varying input constraints. The control algorithm is reduced to an online optimization problem under linear matrix inequality constraints. Copyright © 2012 John Wiley & Sons, Ltd.     

欺骗攻击环境下具有执行器故障的跳变耦合 信息物理系统的同步控制

针对一类离散Markov跳变耦合信息物理系统(CPS)的同步控制问题,在考虑系统参数跳变、耦合参数跳变、控制信息不完全和人为攻击的情况下,设计同步控制器实现CPS的同步.首先,给出具有随机欺骗攻击和执行器故障的Markov跳变耦合CPS模型.其次,基于矩阵Kronecker积,得到同步误差系统,将CPS的同步控制问题转化为同步误差系统的稳定性分析问题.再次,通过构造合适的Lyapunov-Krasovskii泛函,并利用Lyapunov稳定性理论和线性矩阵不等式方法得到使同步误差系统稳定的充分条件,在此基础上,设计同步控制器实现对Markov跳变耦合CPS的同步控制.最后,通过数值仿真例子说明该同步控制器设计方法的有效性.     

Event-triggered robust model predictive control of continuous-time nonlinear systems

The event-triggered control is of compelling features in efficiently exploiting system resources, and thus has found many applications in sensor networks, networked control systems, multi-agent systems and so on. In this paper, we study the event-triggered model predictive control (MPC) problem for continuous-time nonlinear systems subject to bounded disturbances. An event-triggered mechanism is first designed by measuring the error between the system state and its optimal prediction; the event-triggered MPC algorithm that is built upon the triggering mechanism and the dual-mode approach is then designed. The rigorous analysis of the feasibility and stability is conducted, and the sufficient conditions for ensuring the feasibility and stability are developed. We show that the feasibility of the event-triggered MPC algorithm can be guaranteed if, the prediction horizon is designed properly and the disturbances are small enough. Furthermore, it is shown that the stability is related to the prediction horizon, the disturbance bound and the triggering level, and that the state trajectory converges to a robust invariant set under the proposed conditions. Finally, a case study is provided to verify the theoretical results.     

Resilient control of networked control systems under deception attacks: A memory‐event‐triggered communication scheme

This paper considers resilient event‐triggered control problem for a class of networked systems subject to randomly occurring deception attacks. First, a new memory event‐triggered scheme (METS) is proposed to reduce the utilization of communication resources while maintaining desired system performance. Different from some existing event‐triggered schemes, some recently released packets are firstly utilized in the proposed METS, which provides a better flexibility to improve the system dynamics. Second, considering the security problem of the networked control systems, a randomly occurring deception attack model is employed where the bounded malicious signals are injected by the adversary. Considering both the effects of METS and deception attack, new type of networked control system model is constructed and the corresponding memory state‐feedback controller is designed. Then, sufficient conditions for the asymptotical stability of the systems are derived by using a Lyapunov functional technique. Finally, the obtained results are verified through a pendulum system, which demonstrates the effectiveness of the proposed methods.     

欺骗攻击下网络化系统事件触发安全控制

针对网络控制系统受欺骗攻击问题,提出了事件触发器与安全控制器协同设计策略。提出了离散事件触发机制,克服了连续事件触发机制需要新增专用硬件且需要复杂计算避免芝诺现象局限。建立了有机融合欺骗攻击、事件触发机制、网络诱导延时及外部扰动多约束参数的闭环系统时滞模型,推导出了欺骗攻击下事件触发控制系统渐近稳定的充分条件,得到了事件触发器与安全控制器的协同设计条件。仿真验证了方法的有效性。