This paper studies the fault-tolerant model-free adaptive control (FT-MFAC) problem for a class of single-input single-output (SISO) nonlinear networked control systems (NCSs) under denial-of-service (DoS) attacks. A novel FT-MFAC framework is established with the consideration of DoS attacks and the sensor fault, in which DoS attacks obeying the Bernoulli distribution randomly happen in the sensor-to-controller channel and the sensor fault is approximated by the radial basis function neural network (RBFNN). Based on the proposed framework, an FT-MFAC algorithm that uses only input/output data is proposed to guarantee that the output tracking error is bounded in the sense of mean square. Finally, the effectiveness of the proposed algorithm is illustrated by a simulation.

本文研究了一类单输入单输出（SISO）非线性网络控制系统（NCS）在拒绝服务（DoS）攻击下的容错无模型自适应控制（FT-MFAC）问题。考虑到DoS攻击和传感器故障，建立了一种新的FT-MFAC框架，其中DoS攻击服从伯努利分布随机发生在传感器到控制器的通道中，传感器故障由径向基函数神经网络近似(RBFNN)。基于所提出的框架，提出了一种仅使用输入/输出数据的FT-MFAC算法，以保证输出跟踪误差在均方意义上是有界的。最后，通过仿真说明了所提出算法的有效性。