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A Novel Adaptive NN Prescribed Performance Control for Stochastic Nonlinear Systems.

Shuai Sui, C L Philip Chen, Shaocheng Tong

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    This study introduces a new adaptive control method using neural networks (NNs) for stochastic nonlinear systems. The approach ensures finite-time performance and bounded system variables, simplifying controller design.

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Stochastic Systems

    Background:

    • Stochastic nonlinear systems present significant control challenges due to uncertainties and unmodeled dynamics.
    • Adaptive backstepping control is a powerful technique, but its application to systems with unmodeled dynamics and finite-time performance requirements is complex.

    Purpose of the Study:

    • To design a novel adaptive neural network (NN)-based backstepping control strategy for stochastic nonlinear systems with unmodeled dynamics.
    • To achieve finite-time prescribed performance for the tracking error and ensure system state boundedness.

    Main Methods:

    • Utilized neural networks to approximate unknown functions in the stochastic nonlinear system.
    • Employed changing supply function and dynamical signal function methods to address unmodeled dynamics.
    • Developed a modified finite-time adaptive NN control strategy based on finite-time performance functions (FTPFs).

    Main Results:

    • The proposed control scheme guarantees that all system states are bounded in probability.
    • The tracking error converges to a specified performance range within finite time.
    • The controller design is simplified through the proposed finite-time adaptive NN strategy.

    Conclusions:

    • The novel adaptive prescribed performance tracking control scheme is effective for stochastic nonlinear systems with unmodeled dynamics.
    • The method ensures finite-time convergence and boundedness, verified by simulations.
    • The approach offers a simpler yet robust solution for complex control problems.