Control over imperfect networks: model based predictive networked control systems

Official URL: http://dx.doi.org/10.1109/TIE.2010.2051932

Networked control systems are digital control systems in which the functionality of the sensor, control and actuator reside in physically different computer nodes which communicate over a network. Although it is necessary to put an upper bound to the latency from sensing to actuation in a digital control system, the performance of a basic networked control system is threatened by the data loss and unpredictable delays of the communication network. We have proposed model based predictive networked control systems (MBPNCS) in which such losses are compensated by using a model of the plant within the controller node which, based on the actual or estimated state of the plant, makes a series of control estimates into the future, and sends all of them to the actuator node at once. This way the stability of the plant is maintained even under communication delay and data loss. The system is designed to eliminate the necessity of acknowledgments signifying the success of transmission, since such signals are, in general, also unreliable. In this paper we describe MBPNCS, then introduce a stability criterion. This is followed by computer simulations and experiments involving the speed control of a DC motor.