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    This study introduces a new finite-time (FT) tracking control method for uncertain nonlinear multi-input-multioutput (MIMO) systems with input backlash. The approach ensures fast convergence and robust performance, demonstrating effective tracking control in finite time.

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    Area of Science:

    • Control Systems Engineering
    • Nonlinear Dynamics
    • Artificial Intelligence

    Background:

    • Addressing finite-time (FT) tracking control challenges in uncertain multi-input-multioutput (MIMO) nonlinear systems.
    • Mitigating performance degradation caused by input backlash in control systems.

    Purpose of the Study:

    • To develop an effective FT tracking control strategy for uncertain MIMO nonlinear systems with input backlash.
    • To improve tracking accuracy and system robustness under uncertainties and input nonlinearities.

    Main Methods:

    • Design of a modified FT command filter within a backstepping framework to approximate signal derivatives and suppress chattering.
    • Implementation of an improved FT error compensation mechanism to counteract filtering errors.
    • Application of neural-network-adaptive technology for FT convergence in MIMO systems with input backlash.

    Main Results:

    • The proposed control method achieves faster approximation of virtual signal derivatives and suppresses chattering.
    • The FT error compensation mechanism effectively reduces the impact of filtering errors.
    • Demonstrated finite-time tracking performance for uncertain MIMO nonlinear systems with input backlash via simulation.

    Conclusions:

    • The novel FT tracking control strategy effectively addresses uncertainties and input backlash in MIMO nonlinear systems.
    • The integration of command filtering, error compensation, and neural-network adaptation ensures robust and precise control.
    • The simulation results validate the superiority and effectiveness of the proposed control design method.