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Data-driven trajectory tracking control of UAV systems under a novel probability-selection event-triggered mechanism
Chao Cheng1, Haotong Lv2, Wenxin Sun3
1School of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China.
None:
This paper focuses on data-driven trajectory tracking control of unmanned aerial vehicle (UAV) systems using a probability-selection event-triggered mechanism (PETM). The mechanism consists of two phases: initially, triggered packets are generated using a conventional event-triggered mechanism (ETM), followed by a probability-selection scheme to identify the actual transmitted packet (ATP) from a set of consecutively triggered packets. The PETM efficiently reduces redundant packet transmissions, especially as the UAV system nears stability, thereby alleviating communication channel overload. Based on the data-driven system representation, a stability criterion is derived in terms of linear matrix inequalities. The event-triggering matrix and controller gain are then co-designed without requiring explicit knowledge of the UAV dynamics. This data-driven co-design approach balances system performance with the signal transmission rate. Finally, an illustrative example demonstrates the effectiveness of the developed data-driven tracking control method for UAVs.
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