Zhihao Luo, Wentao Yan, Jingyu Gong, Min Wang, Zhizhong Zhang, Xuhong Wang, Yuan Xie, Xin Tan. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

This article studies a data-driven event-triggered sliding-mode control approach for wind turbines with prescribed performance and quantified information to maximize power generation efficiency. Initially, a partial form of the dynamic linearization model is established for the controlled wind turbine system. A logarithmic quantizer is considered to quantize data before it is transmitted. Then, a data-driven event-triggered sliding-mode control scheme is established, where a smooth function is employed to limit the control error to a prescribed range, and an event-triggered scheme is designed to reduce the communication frequencies of the controlled plant. Finally, the convergence of the formulated approach is rigorously demonstrated, and the simulation results further verify the effectiveness of the developed method.