Control Over Imperfect Networks: Model-Based Predictive Networked Control Systems

Networked control systems (NCSs) are digital control systems in which the functionality of the sensor, control, and actuator reside in physically different computer nodes communicating over a network. However, random delays and data loss of the communication network can endanger the stability of an NCS. We have proposed model-based predictive NCSs (MBPNCSs) that compensate for the aforementioned problems and avoid performance loss using a predictive control scheme based on a model of the plant. There are three main contributions of this paper to existing methods: an NCS that can work under random network delay and data loss with realistic structural assumptions, an explicit mechanism for reducing the effects of network delay and data loss on the deviation of plant state estimates from actual plant states, and an architecture where upstream nodes can work without receiving acknowledge information about the status of previously sent data packets from downstream nodes. In this paper, we describe MBPNCS and then introduce a stability criterion. This is followed by computer simulations and experiments involving the speed control of a dc motor. The results show that considerable improvement over performance is achieved with respect to an event-based NCS.