Time Delay and Data Dropout Compensation in Networked Control Systems Using Extended Kalman Filter

In networked control systems, time delay and data dropout can degrade the performance of the control system and even destabilize the system. In the present paper, the Extended Kalman filter is employed to compensate the effects of time delay and data dropout in feedforward and feedback paths of networked control systems. In the proposed method, the extended Kalman filter is used as an observer with nonlinear discrete model along with a predictor and a compensator. The predictor provides a sequence of predictions for state variables, and the controller generates a set of control predictions in the future based on the predictor outputs. The compensator chooses the best control signal, from among the set of control signals transmitted by the predictor, to compensate for the random network transmission time delay and packet dropout. In the cases with periodic load processes, correlation can be seen in delays between samples, and time delay behavior is captured with the Markov chains model. The dependence between feedback and feedforward delays, are also modeled by letting the distribution of the delays be governed by the state of an underlying Markov chains. Simulation results demonstrate the performance of the presented method in time delay and data dropout compensation for networked control systems.