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Adaptive Neural Command Filtering Control for Nonlinear MIMO Systems With Saturation Input and Unknown Control

Jinpeng Yu, Peng Shi, Chong Lin

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    This summary is machine-generated.

    This study introduces a novel adaptive neural networks (NNs) control method for complex nonlinear systems. The approach effectively handles input saturation and unknown control gains in multiple-input multiple-output (MIMO) systems.

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    Area of Science:

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

    Background:

    • Multiple-Input Multiple-Output (MIMO) systems present significant control challenges due to nonlinearities, input saturation, and unknown control gains.
    • Traditional control methods like backstepping suffer from computational complexity and limitations in handling dynamic surface uncertainties.
    • Adaptive control strategies are crucial for systems with uncertain parameters and external disturbances.

    Purpose of the Study:

    • To develop an advanced control strategy for MIMO nonlinear systems with input saturation and unknown control gains.
    • To address the
    • explosion of complexity
    • issue inherent in recursive backstepping designs.
    • To enhance the robustness and performance of tracking control in complex dynamic systems.

    Main Methods:

    • A command filtered adaptive neural networks (NNs) control approach is proposed.
    • Virtual controllers and error compensation signals are designed to manage system dynamics.
    • Neural networks are employed to approximate system nonlinearities.
    • Nussbaum-type functions are integrated to address unknown control gains.

    Main Results:

    • The proposed method successfully manages input saturation and unknown control gains in MIMO nonlinear systems.
    • Command filtering effectively mitigates the computational complexity associated with traditional backstepping.
    • The developed error compensation signals overcome limitations of the dynamic surface method.
    • Simulation examples demonstrate the efficacy and robustness of the new control scheme.

    Conclusions:

    • The presented command filtered adaptive neural networks control method offers a viable solution for challenging MIMO nonlinear systems.
    • This approach enhances tracking control performance by effectively handling system uncertainties and constraints.
    • The study validates the practical applicability of the proposed control scheme through simulation.