Fractional Order Networked Control System using Stochastic Iterative Learning Control

Networked control systems (NCSs) are one of the main areas in control and computer engineering. Despite the many advantages that NCSs have, they impose restrictions such as random data dropout on control systems. We can remove the impact of data dropout by employing the iterative learning control (ILC) method for repetitive systems. In this paper, an ILC for stochastic fractional order NCSs has been presented to deal with the random data dropout imposed by the network. Using the Kalman filter approach, the optimal learning gain is determined so that the system output tracks to the desired output in the presence of random data dropout. The simulation results show that the mean of tracking error becomes close to zero after 500 iterations when a significant number of data packets have been lost in the network.