Predictive Control Applied to Networked Control Systems

Abstract

IntroductionThe researches of the networked control systems (NCSs) cover a broader, more complex technology, because that networked control systems relate to computer network, communication, control, and other interdisciplinary fields. Networked control systems have become one of the hot spots of international control areas in recent years. The networked control system theoretical research is far behind its application, so the networked control system theory study has important academic value and economic benefits at present.NCSs performance is not only related with the control algorithms, but also the network environment and the scheduling algorithms. The purpose of network scheduling is to avoid network conflicts and congestion, accordingly reducing the network-induced delay, packet loss rate and so on, which can ensure the better network environment. If the case, where the data cannot be scheduled, appears in the network, the control algorithm has not fundamentally improved the performance of the system, thus only adjusting data transmission priorities and instants over the network by using the scheduling algorithms, in order to make the whole system to achieve the desired performance.Along with the networked control system further research, people gradually realized that the scheduling performance must be taken into account when they research control algorithms, that is, considering the two aspects of scheduling and control synthetically. The joint design of both scheduling performance and control performance is concerned by the majority of researchers (Gaid M B et al., 2006a(Gaid M B et al., ,2006bArzen K E et al., 2000). Therefore, NCSs resource scheduling algorithms, as well as scheduling and control co-design are the main research directions and research focus.The generalized predictive control and the EDF (Earliest Deadline First) scheduling algorithm are adopted by the NCSs co-design in this chapter. The co-design method www.intechopen.com Frontiers of Model Predictive Control