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

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.