Networks and their applications have been evolving substantially in the last two decades. Networked Control Systems (NCS) are one of the hottest topics in networkbased applications. By networked control system we mean controlling the system through the network. Such systems offer advantages such as lower installation costs, increased flexibility, reliability, also reduced maintenance costs. The change of communication architecture from point-to-point to this common bus approach, however, introduces different forms of loss of packets in the closed-loop system dynamics. These packets loss comes from node failures or message collisions.

In real time systems, particularly control systems, delays or dropped packets may cause performance degradation and system destabilization. In order to consider the uncertainty of communication delays and packet losses, intelligent computational approaches such as fuzzy logic, neural networks, and genetic algorithm can be used. In this paper, The effect of time delay is compensated via building undelayed plant model based on delayed model data using the Adaptive Linear Neuron networks (ADALINE). In ADALINE the linear networks are adjusted at each time step based on new input and target vectors which can find weights and biases that minimize the network's sum-squared error for recent input and target vectors. The proposed works are applied on distributed control of a DC servo system. The network is built using the true time MATLAB toolbox. Several simulation examples are applied using CAN network to clarify the efficiency of the proposed methods.