Mathematics, Год журнала: 2025, Номер 13(6), С. 976 - 976
Опубликована: Март 16, 2025
This study presents an advanced Sliding Variable-Based Robust Adaptive Control (SVRAC) scheme designed for canonical nonlinear system with unknown dynamic and control gain functions. Leveraging neural network (NN) approximation, the proposed method simplifies design by eliminating need traditional sliding mode (SMC) components like equivalent switching controls. SVRAC integrates three key elements: a feedback term to stabilize errors, NN-based estimate compensate uncertainties, robustness adjustment maintain integrity under variations. Theoretical validation through Lyapunov stability analysis confirms that errors are Semi-Globally Uniformly Ultimately Bounded (SGUUB), tracking error converges neighborhood of zero. Numerical engineering simulations further demonstrate achieves superior performance, robustness, adaptability compared conventional methods. approach offers streamlined yet effective solution managing uncertainties in complex systems, potential applications across diverse domains.
Язык: Английский