标题： 多输入多输出非线性控制系统中鲁棒零动态异常行为的在线检测与抑制 显示英文标题

标题： Online Detection and Mitigation of Robust Zero Dynamics Anomaly Behavior in MIMO Nonlinear Control Systems

摘要： 本文提出了一种方法，用于检测鲁棒零动态异常行为，并减轻其对一般多输入多输出（MIMO）非线性系统的影响。 所提出的方案无需依赖精确的动力学模型即可保证闭环系统的韧性与稳定性。 该方法分两个阶段运行。 首先，它通过测量系统输入与模型输入之间的差异作为残差信号来检测异常行为。 在检测到攻击后，生成恢复信号以将系统恢复到正常状态。 在此阶段，使用神经网络模型来估计异常信号并恢复闭环系统。 神经网络模型的权重在线更新，使用自适应规则而无需提前训练数据。 通过在四储罐系统上模拟各种场景，验证了所提方法的准确性和性能。 显示英文摘要

摘要： This paper presents a methodology to detect robust zero dynamics anomaly behavior and mitigate the impacts in general multi-input multi-output (MIMO) nonlinear systems. The proposed method guarantees the resiliency and stability of the closed-loop system without relying on an accurate dynamical model. The presented method operates in two stages. First, it measures the difference between the system input and that of the model as a residual signal to detect the anomaly behavior. After detecting the attack, a recovery signal is generated to restore the system to its nominal condition. In this stage, a neural network model is used to estimate the anomaly signal and recover the closed-loop system. The weights of the neural network model are updated online using adaptation rules without needing prior data for training. The accuracy and performance of the proposed methods are verified by simulating various scenarios on a fourtank system.