Quantized data-based iterative learning control under denial-of-service attacks

This article mainly studies the quantized data-based iterative learning tracking control (QDBILTC) problem of nonlinear networked control systems in the presence of signals quantization and denial-of-service (DoS) attacks. The quantizer considered here is static with the logarithmic form. First, an estimate output attack compensation mechanism is designed to compensate for the effect of DoS attacks based on the extended dynamic linearization method. Then, a QDBILTC algorithm is developed to guarantee the system tracking performance and the bounded input and bounded output stability in mean-square sense. The process of designing the QDBILTC algorithm only uses the input and output data of the system, and the proof of which uses the compression mapping principle and the mathematical induction. The effectiveness of the proposed QDBILTC algorithm is illustrated by a digital simulation.

Automated summary

This article mainly studies the problem of quantized data-based iterative learning tracking control (QDBILTC) for nonlinear networked control systems in the presence of signals quantization and denial-of-service (DoS) attacks. A corresponding algorithm is proposed to solve this problem, and its effectiveness is demonstrated through digital simulations.