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    This study introduces a novel critic learning method for decentralized control of nonlinear systems. The approach uses neural networks and concurrent learning to ensure system stability and overcome common control limitations.

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    Area of Science:

    • Control Theory
    • Nonlinear Systems
    • Artificial Intelligence

    Background:

    • Decentralized control of continuous-time nonlinear systems with mismatched interconnections presents significant challenges.
    • Existing methods often require strict conditions like initial admissible control and persistence-of-excitation.

    Purpose of the Study:

    • To develop a novel critic learning method for decentralized control of nonlinear systems.
    • To overcome limitations of existing control methods by relaxing stringent conditions.

    Main Methods:

    • Converted the decentralized control problem into optimal control problems using discounted cost functions for auxiliary subsystems.
    • Developed a critic learning approach utilizing critic neural networks (NNs) and a modified gradient descent method with concurrent learning to solve Hamilton-Jacobi-Bellman equations (HJBEs).
    • Employed Lyapunov's direct method to prove stability of NN weight estimation errors and closed-loop auxiliary systems.

    Main Results:

    • The novel critic learning method successfully solves the HJBEs for decentralized control.
    • The method removes the need for initial admissible control and relaxes the persistence-of-excitation condition.
    • Demonstrated uniform ultimate boundedness for NN weight estimation error and system states, ensuring stability.

    Conclusions:

    • The proposed critic learning approach is effective for decentralized control of nonlinear systems with mismatched interconnections.
    • Validated the method using a nonlinear-interconnected plant and an unstable interconnected power system.
    • Offers a more practical and less restrictive solution for complex control problems.