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Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
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Adaptive Neural-Based SMC for Singularly Perturbed Systems With Dead Zone Under Aperiodic Sampling.

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    This study introduces an adaptive neural network (NN) sliding-mode control (SMC) for systems with irregular sampling and input dead zones. The method ensures system stability and performance despite these challenging conditions.

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence in Control
    • Nonlinear Systems Theory

    Background:

    • Sampled-data systems often face challenges with aperiodic sampling intervals, introducing complexity in control design.
    • Input dead zone nonlinearities significantly degrade system performance and robustness.
    • Singularly perturbed systems require specialized control strategies due to their fast and slow dynamics.

    Purpose of the Study:

    • To develop an adaptive neural network (NN)-based sliding-mode control (SMC) strategy for sampled-data singularly perturbed systems.
    • To address challenges posed by aperiodic sampling intervals and input dead zone nonlinearities.
    • To guarantee system stability and performance under the specified challenging conditions.

    Main Methods:

    • A nonhomogeneous sojourn probability approach is employed to model the irregular sampling intervals.
    • An adaptive neural network (NN) scheme is utilized for online estimation and compensation of input dead zone nonlinearities.
    • A novel sliding-mode controller is designed to handle variations in sampling modes and singular perturbation parameters.

    Main Results:

    • The proposed adaptive NN-SMC strategy ensures exponential ultimate boundedness of system states in the mean-square sense.
    • The control strategy guarantees the reachability of the predefined sliding surface in the closed-loop system.
    • The effectiveness of the proposed control method is validated through a practical example.

    Conclusions:

    • The developed adaptive NN-SMC effectively handles aperiodic sampling and input dead zones in singularly perturbed systems.
    • The control approach enhances system robustness and performance, ensuring stability and state convergence.
    • The proposed methodology offers a viable solution for complex control problems in sampled-data systems.