Dynamic Event-Triggered Adaptive Broad Learning for a Two-Degree-of-Freedom Helicopter System with Prescribed Performance

Liang Cao, Yexin Mo, Wei Xiao, Kaili Feng, Zhongzhen Wu and Xiangli Li ()
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Liang Cao: College of Artificial Intelligence, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Yexin Mo: College of Artificial Intelligence, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Wei Xiao: College of Artificial Intelligence, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Kaili Feng: School of Computer Science, Guangdong University of Education, Guangzhou 510310, China
Zhongzhen Wu: College of Agriculture & Biology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Xiangli Li: College of Artificial Intelligence, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China

Mathematics, 2025, vol. 13, issue 21, 1-18

Abstract: This study proposes an adaptive broad learning strategy for a two-degree-of-freedom helicopter system based on specified performance and dynamic event-triggered. First, broad learning is employed to approximate system uncertainties. Compared to radial basis function neural networks, broad learning achieves this by increasing the number of nodes, enabling it to approximate system uncertainties with smaller tracking errors. At the same time, an error transformation method is employed to guarantee that the tracking error adheres to a predefined performance function. Furthermore, a dynamic event-triggering mechanism reduces communication overhead and prevents the Zeno effect. Subsequent Lyapunov-based stability analysis confirms that the system exhibits semi-global consistency, stability, and boundedness. In addition, simulation results verify the proposed control strategy’s effectiveness and robustness.

Keywords: two-degree-of-freedom helicopter; broad learning; prescribed performance; dynamic event triggered (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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