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 profiting from the characteristics of networked Control Systems like the packet-primarily based transmission, timestamp technique, as well as sensible sensor and actuator, a knowledge-based networked predictive management (DBNPC) technique is proposed to actively make amends for the two-channel packet dropouts, where solely the input and output data of the non-linear plant are needed. A sufficient condition for the soundness of the closed-loop system is developed. Furthermore, the resulting DBNPC system will 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.

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