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In an open-loop system, such as a basic thermostat, the poles of the transfer function influence the system's response but do not determine its stability. However, when feedback is introduced to form a closed-loop system, such as an advanced thermostat that adjusts heating based on room temperature, stability is governed by the new poles of the closed-loop transfer function.
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An Online Event-Triggered Near-Optimal Controller for Nash Solution in Interconnected System.

Narendra Kumar Dhar, Nishchal Kumar Verma, Laxmidhar Behera

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    Summary
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    This study introduces an event-triggered controller for nonlinear systems, achieving near-optimal control and stability. The method reduces control updates by up to 27% in HVAC systems.

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Game Theory

    Background:

    • Interconnected nonlinear discrete-time systems often involve complex, multi-agent scenarios.
    • Achieving optimal control in such systems while ensuring stability and efficiency is a significant challenge.
    • Traditional control methods may require frequent updates, leading to computational inefficiency.

    Purpose of the Study:

    • To propose a real-time, event-triggered, near-optimal controller for nonlinear discrete-time interconnected systems.
    • To ensure system stability and achieve a Nash equilibrium in a nonzero-sum game context.
    • To reduce the frequency of control updates through an event-based methodology.

    Main Methods:

    • Developed an event-based controller utilizing actor-critic neural networks for online policy generation.
    • Implemented an event-triggering condition derived from Lyapunov stability analysis.
    • Guaranteed lower bounds on interevent time and boundedness of closed-loop parameters.

    Main Results:

    • The proposed controller achieves near-optimal control policies and ensures system stability.
    • Control update instants were minimized, reducing them by up to 27% in a practical HVAC system.
    • Validated the controller's efficacy in maintaining desired temperature setpoints across multiple building zones.

    Conclusions:

    • The event-triggered, near-optimal controller effectively manages nonlinear discrete-time interconnected systems.
    • The methodology ensures stability, optimality, and significant reductions in control update frequency.
    • This approach offers a computationally efficient solution for complex control problems, demonstrated in HVAC applications.