Security Indirect Adaptive Iterative Learning Control for Nonlinear Systems with Fading Channel Under DoS Attacks

This paper investigates the security data-driven control problem for repetitive nonlinear systems with fading channels under denial-of-service (DoS) attacks. Firstly, under the influence of fading channels, a novel data-driven indirect adaptive iterative learning control (iAILC) algorithm is designed to idealize system tracking results. The adaptive mechanism is introduced to construct the adaptive set-point tuning law, thereby regulating the learning gain instantaneously. Secondly, double dynamic linearization method is deployed to simplify the system model, wherein the considered system is transferred to linear parametric model related to data. Then, considering the stochastic DoS attacks act on the fading output, compensation mechanism is developed by historical data, which eases the effect of DoS attacks accordingly. Finally, simulation results validate the efficiency of the presented control method.