Iterative learning fault-tolerant control for the networked control systems with initial state disturbance

The iterative learning fault-tolerant control strategies with non-strict repetitive initial state disturbances are studied for the linear discrete networked control systems (NCSs) and the nonlinear discrete NCSs. In order to reduce the influence of the initial state disturbance in iteration, for the linear NCSs, considering the external disturbance and actuator failure, the iterative learning fault-tolerant control strategy with impulse function is proposed. For the nonlinear NCSs, the external disturbance, packet loss and actuator failure are considered, the iterative learning fault-tolerant control strategy with random Bernoulli sequence is provided. Finally, the proposed control strategies are used for simulation research for the linear NCSs and the nonlinear NCSs. The results show that both strategies can reduce the influence of the initial state disturbance on the tracking effect, which verifies the effectiveness of the given method.