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Distributed Loads: Problem Solving01:21

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Ampere-Maxwell's Law: Problem-Solving01:17

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A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Data-Driven Learning Distributed Optimization of Heterogeneous Linear Multiagent Systems.

Haizhou Yang, Kedi Xie, Maobin Lu

    IEEE Transactions on Cybernetics
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    Summary
    This summary is machine-generated.

    This study introduces a data-driven adaptive dynamic programming (ADP) method for distributed optimization in multiagent systems, eliminating the need for prior system knowledge. The approach ensures agents reach optimal consensus, demonstrated in hydraulic turbine control.

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    Last Updated: Jan 14, 2026

    Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
    11:53

    Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

    Published on: December 9, 2012

    13.4K

    Area of Science:

    • Control Systems Engineering
    • Optimization Theory
    • Artificial Intelligence

    Background:

    • Distributed optimization is crucial for multiagent systems.
    • Heterogeneous systems and directed networks present unique challenges.
    • Existing methods often require complete knowledge of system dynamics.

    Purpose of the Study:

    • To develop a data-driven distributed optimization control law for heterogeneous linear multiagent systems.
    • To overcome the limitation of unknown system dynamics.
    • To achieve optimal output consensus among agents.

    Main Methods:

    • Adaptive Dynamic Programming (ADP) based data-driven approach.
    • Determination of feedback gain from state and input data.
    • Reconstruction of system dynamics using feedback gain and running data.
    • Design of control law parameters via steady-state equations.

    Main Results:

    • A novel distributed optimization control law is developed without prior system dynamics knowledge.
    • The certainty equivalence principle guarantees convergence to the optimal solution.
    • Output consensus is achieved for all agents at the global cost function's optimum.

    Conclusions:

    • The proposed data-driven ADP approach effectively solves distributed optimization for heterogeneous multiagent systems.
    • The method is validated through application to hydraulic turbine power sharing control.
    • This approach offers a robust solution for complex networked systems with unknown dynamics.