Model Based Predictive Networked Control Systems

Networked control systems where the sensors, controller and actuators of a digital control system reside on different computer nodes linked by a network, aim to overcome the disadvantages of conventional digital control systems at the application level, such as difficulty of modification, vulnerability to electrical noise, difficulty in maintenance and upgrades. However random communication delay and loss on the network may jeopardize stability since the communication delay decreases the phase margin of the control system and data loss can be considered as noise. In this project, we propose a novel networked control method where satisfactory control is possible even under random delay and data loss. We keep a model of the plant inside the controller node and use it to predict the plant states into the future to generate corresponding control outputs. At every sampling period the states of the model are reset to the actual or predicted states of the plant. The ambiguity of plant state during periods of total communication loss are also addressed. The proposed model based predictive networked control system architecture is first verified by simulation on the model of a DC motor under conditions of data loss and noise. Then experiments are repeated on a dedicated test platform using a physical DC motor. Results show that significant amounts of data loss and delay can be tolerated in the system before performance starts to degrade.