SCHMM-based modeling and prediction of random delays in networked control systems

Abstract

This paper is concerned with the modeling and prediction of random delays in networked control systems. Considering the Gaussian mixture distribution followed by the random delays in real networks, the semi-continuous hidden Markov model (SCHMM) is proposed in this paper to model the random delays. The initialization and optimization problems of the model parameters are solved by using the K-mean clustering algorithm and the expectation maximization algorithm. Based on the model, the prediction of the controller-to-actuator (CA) delay in the current sampling period is obtained. The prediction can be used to design a controller to compensate the CA delay in the future research. Two illustrative examples are given to demonstrate the effectiveness and superiority of the proposed modeling and prediction methods.