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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Nonlinear Dynamics

    Background:

    • Multiagent systems require robust control strategies, especially with actuator faults.
    • Estimating unmeasured states is crucial for effective control in complex systems.

    Purpose of the Study:

    • To develop an adaptive neural containment control for nonlinear multiagent systems with actuator faults.
    • To enhance system performance and reduce computational load.

    Main Methods:

    • Utilizing neural networks for state estimation via a neuro-adaptive observer.
    • Designing an event-triggered control law to minimize computational burden.
    • Employing Lyapunov stability theory for system analysis.

    Main Results:

    • The closed-loop system achieves cooperative semiglobal uniform ultimate boundedness (CSGUUB).
    • Followers' outputs successfully reach the convex hull formed by leaders.
    • Containment errors are bounded within a finite time.

    Conclusions:

    • The proposed neuro-adaptive control scheme effectively addresses actuator faults in nonlinear multiagent systems.
    • The event-triggered approach offers computational efficiency.
    • Finite-time containment is achieved, validating the control strategy.