Adaptive Neural-Based SMC for Singularly Perturbed Systems With Dead Zone Under Aperiodic Sampling

This article addresses the adaptive neural network (NN)-based sliding-mode control (SMC) problem for sampled-data singularly perturbed systems under aperiodic sampling intervals and input dead zone nonlinearities. To accurately characterize the irregularity of sampling intervals, a nonhomogeneous sojourn probability approach is introduced. To accurately characterize the irregularity of sampling intervals, a nonhomogeneous sojourn probability approach is introduced. An adaptive NN scheme is utilized to estimate and effectively compensate for the nonlinear errors induced by input dead zones, thereby significantly enhancing the robustness and performance of the controlled system. Leveraging these considerations, a novel sliding-mode controller, specifically designed to accommodate variations in sampling period modes and singular perturbation parameters, is proposed. This control strategy guarantees the exponential ultimate boundedness of system states in the mean-square sense and ensures the reachability of the predefined sliding surface in the closed-loop system. The validity of the proposed theory is demonstrated through a practical example.