BP neural network prediction-based variable-period sampling approach for networked control systems

Abstract

The biggest problem that networked control systems face is the random time-varying delay, which often causes system instability and even collapse. Aiming at this problem, a new modeling scheme for the networked control systems, motivated from a variable-period sampling approach, is presented in this paper. Here, the time delay to occur at current sampling step is taken as the sampling period between current sampling step and next sampling step. To predict online the time delay induced in the networked control systems, a BP feedforward neural network is adopted and the training algorithm of the BP neural network is given. To make the BP neural network adapt to the changing environment of the networked control systems and improve its prediction accuracy, the BP neural network is designed to further update according to its prediction error after each prediction. At each sampling step, good approximation to actual time delay becomes available and different sampling period is obtained. Control simulations using the variable sampling period and fixed sampling period are compared. Simulation results show that this new approach can alleviate the influence of time delay to the greatest extent and improve the performance of the networked control systems.