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Event-Triggered Near-Optimal Control for Unknown Discrete-Time Nonlinear Systems Using Parallel Control.

Jingwei Lu, Qinglai Wei, Tianmin Zhou

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    Summary
    This summary is machine-generated.

    This study introduces an event-triggered near-optimal control (ETNOC) method for unknown nonlinear systems. The novel approach ensures system stability and performance using parallel control and adaptive dynamic programming.

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    Area of Science:

    • Control Systems Engineering
    • Nonlinear System Analysis
    • Adaptive Dynamic Programming

    Background:

    • Investigating control strategies for unknown discrete-time nonlinear systems presents significant challenges.
    • Existing methods often require complete system knowledge or lack event-triggered mechanisms for efficiency.
    • Event-triggered control aims to reduce computational load by activating control actions only when necessary.

    Purpose of the Study:

    • To develop an event-triggered near-optimal control (ETNOC) method for unknown discrete-time nonlinear systems.
    • To introduce a parallel control framework with an augmented nonlinear system (ANS) and augmented performance index (API).
    • To design an online learning algorithm for implementing ETNOC without system reconstruction.

    Main Methods:

    • Proposed an augmented nonlinear system (ANS) and augmented performance index (API) to facilitate parallel control.
    • Developed a novel event-triggered scheme and ETNOC method based on the ANS and API.
    • Employed neural network (NN) and adaptive dynamic programming (ADP) techniques within an online learning algorithm.

    Main Results:

    • Established the control stability relationship between the ANS and the original system.
    • Proved the control stability of the developed ETNOC method and provided an upper bound for the performance index.
    • Demonstrated the convergence of signals in the closed-loop system (CLS) using Lyapunov analysis without input boundedness assumptions.

    Conclusions:

    • The proposed parallel control and event-triggered scheme enable near-optimal control for unknown discrete-time nonlinear systems.
    • The developed online learning algorithm effectively implements ETNOC using NN and ADP techniques.
    • Simulation results validate the theoretical findings and the efficacy of the proposed ETNOC method.