Design of Two DOF digital servo system with RNN-based dynamic state predictive control for network control systems with large delays

Deployment of the Internet of Things (IoT) in industries have enabled remote control of various mechanical system over the Internet. It is known that such networked control systems (NCSs) might suffer large time-varying communication delays. Not only performance degradation of the controlled system but also stability issues may occur as a consequence. The present paper proposes a two degree-of-freedom (DOF) digital servo system that stabilizes a feedback control system under the presence of large data transmission delay. We formulated a round trip time (RTT) predictor using a recurrent neural network (RNN). We then designed a dynamic state prediction controller (DSPC) to stabilize the feedback control system under the presence of large delay without causing control performance degradation. The controller gain is determined by solving LMI-based delay-dependent optimization problem. The designed controller is applied to a virtual inverted pendulum attached to the tip of an industrial robot manipulator controlled through a network that includes a wifi router. Results of experiments revealed that the proposed controller exhibited good performance under large fluctuation of RTT.