Lihua Wu,∗ Jie Bai,∗ Jiecheng Li,∗ Guohao Huang,∗ and Huiyuan Wu∗
Neural network control, hysteresis nonlinearity, time-varying state constraints, two-degree-of-freedom (2-DOF) helicopter system
An adaptive parameter control method is introduced for a two- degree-of-freedom helicopter system, considering input hysteresis nonlinearity and time-varying state constraints. First, at each step of the backstepping design process, time-varying barrier Lyapunov functions are formulated to ensure that all state variables remain within the time-varying constraints. Second, the adaptive controller, designed by incorporating Radial Basis Function neural networks and bounded estimation methods, effectively addresses system uncertainties and the impact of hysteresis nonlinearity. A rigorous stability analysis using the Lyapunov method is conducted to ensure that signals in the closed-loop system remain bounded and that the time-varying state constraints are met. The proposed control strategy is designed to not only achieve tracking of the output variables but also to improve the system’s robustness in dynamic environments. Finally, the effectiveness and reliability of the controller design are validated through a comparison of simulation and experimental results.
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