Model based predictive network control systems

The advantages of networked control systems are diverse and address many of the demands of industrial development. As more and more sophisticated problems arise, networked control systems will not only become a convenience or an advantage but an indispensable necessity. However usage of networked control systems introduces time-delay uncertainty in closedloop system dynamics. These time delays are caused by the time sharing of the communication medium as well as computation time necessary for control algorithms and digital to analog conversions and have a destabilizing e ect on system performance jeopardizing system stability. In this work a novel networked control system architecture that runs under non ideal network conditions where packet loss and noise is introduced. The proposed network control system architecture utilizes the computer, control and network disciplines to compensate for each others' inadequacies. The architecture is independent of the control algorithm used and uses a model to predict the plant states into the future to generate corresponding control outputs. This approach enables the system to be controlled in a pre-simulated manner and stability can be maintained even with high packet loss probabilities. Synchronization of the states of plant model and actual plant becomes a major problem in this approach. The proposed model based predictive networked control system architecture is simulated on a DC motor. The overall effect is that stability is maintained although the response of the plant to the reference can be delayed.