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    This study introduces an event-triggered safe critic learning control (ESCLC) algorithm for nonlinear systems with asymmetric state constraints. The ESCLC algorithm ensures system safety and stability while optimizing control policies efficiently.

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    Area of Science:

    • Control Theory
    • Nonlinear Systems
    • Artificial Intelligence

    Background:

    • Nonlinear systems with asymmetric state constraints pose significant control challenges.
    • Ensuring system safety and stability is paramount in control system design.
    • Resource-constrained environments necessitate efficient control strategies.

    Purpose of the Study:

    • To develop an event-triggered safe critic learning control (ESCLC) algorithm for nonlinear systems.
    • To guarantee system safety under asymmetric state constraints.
    • To enhance control policy optimization in resource-constrained scenarios.

    Main Methods:

    • Integration of a safe critic learning control (SCLC) framework with an event-triggering mechanism.
    • Incorporation of control barrier functions into safe value function design.
    • Value iteration for convergence analysis and policy admissibility criteria.
    • Particle swarm optimization for policy improvement, independent of the system control matrix.

    Main Results:

    • The developed SCLC algorithm guarantees system safety and establishes convergence criteria.
    • The ESCLC algorithm ensures asymptotic stability of the closed-loop system.
    • An upper bound on the actual value function is derived, ensuring bounded performance degradation.
    • The policy improvement method removes dependence on the system control matrix.

    Conclusions:

    • The ESCLC algorithm effectively addresses nonlinear systems with asymmetric state constraints.
    • The event-triggering mechanism enhances applicability in resource-limited situations.
    • Simulation results on torsion pendulum and ball-and-beam systems validate the algorithm's effectiveness.