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Output Feedback Control for Stochastic Nonlinear Systems With Nondifferentiable Measurement Function and Input

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    This study addresses stochastic nonlinear systems with input saturation and nondifferentiable measurements. A new power-auxiliary system ensures bounded signals in the closed-loop system, demonstrating effective control strategies.

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    Area of Science:

    • Control Theory
    • Nonlinear Systems
    • Stochastic Systems

    Background:

    • Stochastic nonlinear systems present challenges in control design.
    • Nondifferentiable measurement functions and input saturation complicate controller synthesis.
    • Existing methods often require strong assumptions on system nonlinearities.

    Purpose of the Study:

    • To develop an output feedback control strategy for stochastic nonlinear systems.
    • To address the issues of nondifferentiable measurement functions and input saturation.
    • To relax common growth assumptions on nonlinear terms.

    Main Methods:

    • Introduction of a novel power-auxiliary system to mitigate input saturation effects.
    • Utilizing a key lemma to remove restrictive growth assumptions on nonlinear terms.
    • Construction of an output feedback controller.

    Main Results:

    • The proposed controller ensures that all signals in the closed-loop system are globally bounded almost surely.
    • The controller effectively handles input saturation and nondifferentiable measurement functions.
    • Simulation results validate the efficacy of the developed control strategy.

    Conclusions:

    • The novel approach provides a robust output feedback control solution for complex stochastic nonlinear systems.
    • The method successfully overcomes limitations of existing control techniques by relaxing growth assumptions.
    • The demonstrated effectiveness through simulation highlights the practical applicability of the control strategy.