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Updated: Mar 20, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior
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Iterative Adaptive Dynamic Programming for Solving Unknown Nonlinear Zero-Sum Game Based on Online Data.

Yuanheng Zhu, Dongbin Zhao, Xiangjun Li

    IEEE Transactions on Neural Networks and Learning Systems
    |June 2, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an iterative adaptive dynamic programming algorithm for unknown nonlinear zero-sum games (ZSG) using online data. The method efficiently solves control problems with unknown dynamics, reducing measurement time.

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    Last Updated: Mar 20, 2026

    The HoneyComb Paradigm for Research on Collective Human Behavior
    06:48

    The HoneyComb Paradigm for Research on Collective Human Behavior

    Published on: January 19, 2019

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    Area of Science:

    • Control Systems Engineering
    • Adaptive Control Theory
    • Optimization Techniques

    Background:

    • H-infinity control is vital for disturbance attenuation but requires solving zero-sum games (ZSG).
    • Practical control systems often have unknown dynamics, hindering traditional ZSG solutions.
    • Existing methods struggle with unknown nonlinear dynamics and data inefficiency.

    Purpose of the Study:

    • To develop a model-free algorithm for continuous-time, unknown nonlinear ZSG using only online data.
    • To address limitations of existing methods in handling system uncertainties.
    • To improve efficiency and reduce online measurement time in control system design.

    Main Methods:

    • An iterative adaptive dynamic programming algorithm based on policy iteration.
    • A model-free approach to the Hamilton-Jacobi-Isaacs equation.
    • Approximation of control/disturbance policies and value functions using neural networks (NNs) in a critic-actor-disturber structure.
    • Solving NN weights using the least-squares method.

    Main Results:

    • The proposed algorithm converges uniformly to the optimal solution, akin to a Gauss-Newton method.
    • Neural network weights are efficiently solved via least-squares.
    • Online data can be reused, enhancing computational efficiency.
    • Simulations confirm feasibility for unknown nonlinear ZSG.

    Conclusions:

    • The novel algorithm effectively solves continuous-time, unknown nonlinear ZSG using online data.
    • It offers a significant reduction in online measurement time compared to other algorithms.
    • This approach provides a robust and efficient solution for practical control system challenges with model uncertainties.