Enhanced Sliding Variable-Based Robust Adaptive Control for Canonical Nonlinear System with Unknown Dynamic and Control Gain

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 control design by eliminating the need for traditional sliding mode control (SMC) components like equivalent and switching controls. SVRAC integrates three key elements: a feedback control term to stabilize system errors, a NN-based term to estimate and compensate for uncertainties, and a robustness adjustment term to maintain control integrity under dynamic variations. Theoretical validation through Lyapunov stability analysis confirms that the system errors are Semi-Globally Uniformly Ultimately Bounded (SGUUB), and the tracking error converges to a neighborhood of zero. Numerical and engineering simulations further demonstrate that SVRAC achieves superior tracking performance, robustness, and adaptability compared to conventional methods. This approach offers a streamlined yet effective solution for managing uncertainties in complex nonlinear systems, with potential applications across diverse engineering domains.