In this paper, the robust security protection control for switched systems is studied. A robust anti-disturbance mechanism using the Radial Basis Function Neural Network (RBFNN) is proposed for switched systems, which can approximate and compensate for the impact of unknown disturbance on the system state. Then, a network security protection mechanism based on encoder and decoder is presented, which has the ability to resist the dual impact on the feedback information caused by the network privacy snooping and data injection attacks. Accordingly, stability analysis and state-feedback controller design are given for the switched systems under unknown disturbance, network privacy snooping and injection attacks. Finally, simulation results illustrate the effectiveness of the proposed method.

中文翻译：

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针对注入攻击下的交换系统提供具有受保护反馈信息的鲁棒控制

本文研究了交换系统的鲁棒安全保护控制。针对切换系统，提出了一种基于径向基函数神经网络（RBFNN）的鲁棒抗扰机制，可以近似并补偿未知扰动对系统状态的影响。然后，提出了一种基于编码器和解码器的网络安全保护机制，能够抵抗网络隐私窥探和数据注入攻击对反馈信息的双重影响。据此，给出了切换系统在未知干扰、网络隐私窥探和注入攻击下的稳定性分析和状态反馈控制器设计。最后，仿真结果说明了该方法的有效性。