Data-based predictive control for networked non-linear systems with two-channel packet dropouts

This study is concerned with the data-based control of networked non-linear control systems with random packet dropouts in both the sensor-to-controller and controller-to-actuator channels. By taking advantage of the characteristics of networked control systems such as the packet-based transmission, timestamp technique, as well as smart sensor and actuator, a data-based networked predictive control (DBNPC) method is proposed to actively compensate for the two-channel packet dropouts, where only the input and output data of the non-linear plant are required.

A sufficient condition for the stability of the closed-loop system is developed. Furthermore, the resulting DBNPC system can achieve a zero steady-state output tracking error for step commands. Finally, extensive simulation results on a networked non-linear system demonstrate the effectiveness of the proposed method.