Data-Based Security Fault Tolerant Iterative Learning Control under Denial-of-Service Attacks

This paper mainly studies the data-based security fault tolerant iterative learning control (SFTILC) problem of nonlinear networked control systems (NCSs) under sensor failures and denial-of-service (DoS) attacks. Firstly, the radial basis function neural network (RBFNN) is used to approximate the sensor failure function and a DoS attack compensation mechanism is proposed in the iterative domain to lessen the impact of DoS attacks. Then, using the dynamic linearization technology, the nonlinear system considering failures and network attacks is transformed into a linear data model. Further, based on the designed linearization model, a new data-based SFTILC algorithm is designed to ensure the satisfactory tracking performance of the system. This process only uses the input and output data of the system, and the stability of the system is proved by using the compression mapping principle. Finally, a digital simulation is used to demonstrate the effectiveness of the proposed SFTILC algorithm.