Networked Iterative Learning Fault Diagnosis Algorithm for Systems with Sensor Random Packet Losses, Time-varying Delays, Limited Communication and Actuator Failure : Application to the Hydroturbine Governor System

An iterative learning fault diagnosis (ILFD) algorithm for networked control systems (NCSs) subject to random packet losses, time-varying delays, limited communication and actuator failure is proposed in this paper. Firstly, in order to evaluate the effect of fault on system between every iteration, the information of state error and information of fault tracking estimator from the preceding iteration are used to improve the fault estimation achievement in the actual iteration. The state variable, the Bernoulli process of random packet losses, network communication delay, limited communication and actuator failure are introduced to establish an extended statespace model of the system. Secondly combining Lyapunov stability theory for linear repetitive processes and linear matrix inequality (LMI) technique, new sufficient condition for the existence of an iterative learning fault diagnosis is established. Finally, the feasibility and effectiveness of the proposed design method is illustrated on a dynamic hydroturbine governing system model based on Matlab/Simulink and TrueTime toolbox