Issues on Communication Network Scheduling using Numerical Simulations

Nowadays, industry has successfully used Network Control Systems (NCS) therefore several lines of research have arisen. A NCS is a current application of a Real-Time Distributed Systems (RTDS), composed of a number of nodes capable of developing a complete control process. In this systems several nodes exchange information through a communication network to achieve specific control goals, nevertheless network traffic increases. This affects the overall system performance. Several approaches have been developed to satisfy requirements of both control and communication performance. Particularly, some methodologies focus on saving bandwidth, one of such methodologies is network scheduling. The objective of this methodology is the accurately use of the computing resources. Network scheduling deals with to elect a sampling rate , aiming to reduce the number of data transmitted over the network . The effectiveness of the control system depends on such a sampling rate (Lian et al., 2001; 2002; 2003). A region is acceptable in networked control performance terms if it is contained within two sampling rate boundaries, which can be statistically determined. The use of a common-bus network architecture and a particular network protocol introduces different forms of time delay uncertainties between, sensors, actuators and controllers. Hence, it is quite important to explore different network protocols and network scheduling strategies, before to implement the RTDS, in order to obtain a desired control performance. The main objective of this work is to expose the use of numerical simulations to explore several issues on communication network scheduling of a RTDS, besides to implement a particular network scheduling strategy to evaluate its effectiveness. This is presented using a simulated case study, based upon a 2-DOF helicopter simulation benchmark (Quanser, 2006). This simulation provides an approximation to system response, in which, for demonstration purposes, the main results are obtained for a typical fault scenario. Thus, for this simulation, a scheduling strategies is implemented using TrueTime (Cervin et al., 2003) performing dynamic scheduling.