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    This study introduces adaptive neural controllers for uncertain multiagent systems, including beams and ordinary differential equations (ODEs). The controllers ensure system outputs stay within time-varying constraints despite unknown nonlinearities and backlash.

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Mechanical Engineering

    Background:

    • Multiagent systems with ordinary differential equations (ODEs) and beams exhibit complex dynamics.
    • Unknown nonlinearities and generalized backlash in control signals pose significant challenges for system stability and performance.

    Purpose of the Study:

    • To design adaptive neural controllers for uncertain multiagent systems incorporating ODEs and beams.
    • To ensure system outputs remain within prescribed time-varying constraints despite system uncertainties.

    Main Methods:

    • Neural networks (NNs) are employed to approximate unknown nonlinearities in ODEs and beam dynamics.
    • Novel barrier Lyapunov functions are developed to ensure the boundedness of NN approximation errors.
    • Adaptive neural proportional integral (PI)-type controllers are proposed, with parameters tuned by NNs.

    Main Results:

    • The proposed controllers effectively handle completely unknown nonlinearities and generalized backlash.
    • System outputs are guaranteed to stay within specified time-varying constraints.
    • Demonstrated effectiveness through two illustrative examples.

    Conclusions:

    • The developed adaptive neural control strategy provides robust performance for uncertain multiagent systems.
    • The approach successfully addresses complex nonlinearities and constraints, offering a valuable tool for advanced control applications.