Adaptive Sliding Mode Control for a Class of Uncertain Nonlinear Systems With Saturated Input

In this paper, we propose the novel method for designing the adaptive sliding mode controller for a class of uncertain nonlinear systems with saturated control inputs. To handle the uncertain nonlinear components of the system, adaptive identification laws based on radial basis function neural networks are constructed to estimate the nonlinear dynamics within a surrogate modeling framework. The obtained estimates are directly employed in synthesizing the adaptive sliding mode control law, ensuring accurate tracking of the desired trajectory and enhancing robustness against disturbances and model mismatches. All theoretical results are rigorously proven. To evaluate the effectiveness of the proposed method, simulations are conducted on the specific system. The simulation results demonstrate that the controller achieves high quality tracking of the desired signals and maintains robustness even when the control input is constrained. These findings confirm the potential applicability of the proposed approach to a wide range of practical nonlinear systems.