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    Area of Science:

    • Robotics and Control Systems
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Optimal trajectory tracking is crucial for robotic systems.
    • Uncertain nonlinear systems with state constraints pose significant control challenges.
    • Lifelong learning is essential for adapting control policies in dynamic environments.

    Purpose of the Study:

    • To develop a novel lifelong integral reinforcement learning (LIRL)-based optimal trajectory tracking scheme.
    • To address challenges in uncertain nonlinear continuous-time (CT) affine systems with state constraints.
    • To improve control policy generation and mitigate catastrophic forgetting in multitasking systems.

    Main Methods:

    • Utilized a critic multilayer neural network (MNN) or Deep NN to approximate value functions and generate optimal control policies.
    • Employed a singular value decomposition (SVD)-based method for online tuning of critic MNN weights.
    • Incorporated an online lifelong learning (LL) scheme to prevent catastrophic forgetting.
    • Addressed state constraints using a time-varying barrier function (TVBF).

    Main Results:

    • Achieved optimal trajectory tracking for uncertain nonlinear CT affine systems.
    • Demonstrated effective mitigation of catastrophic forgetting in multitasking systems.
    • Successfully handled state constraints through the TVBF.
    • Showcased uniform ultimate boundedness (UUB) of the closed-loop system via Lyapunov stability analysis.

    Conclusions:

    • The proposed LIRL-based optimal framework effectively addresses trajectory tracking in constrained nonlinear systems.
    • The novel SVD-based tuning and LL scheme enhance control policy adaptability and robustness.
    • Experimental results on a two-link robotic manipulator show a 47% total cost reduction, validating the method's practical effectiveness.