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Related Experiment Videos

Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network.

Hong-Gui Han, Lu Zhang, Ying Hou

    IEEE Transactions on Neural Networks and Learning Systems
    |September 4, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel nonlinear model predictive control (NMPC) using a self-organizing recurrent radial basis function (SR-RBF) neural network for enhanced wastewater treatment process control. The SR-RBF-NMPC system improves dissolved oxygen concentration management.

    Related Experiment Videos

    Area of Science:

    • Control Engineering
    • Artificial Intelligence
    • Environmental Engineering

    Background:

    • Nonlinear systems require advanced control strategies for accurate dynamic behavior prediction.
    • Existing model predictive control methods can be limited by fixed model structures.
    • Wastewater treatment processes (WWTP) demand precise control of parameters like dissolved oxygen (DO).

    Purpose of the Study:

    • To develop a nonlinear model predictive control (NMPC) scheme utilizing a self-organizing recurrent radial basis function (SR-RBF) neural network.
    • To concurrently adjust the structure and parameters of the SR-RBF neural network for improved nonlinear system modeling.
    • To enhance the control performance of dissolved oxygen (DO) concentration in wastewater treatment processes (WWTP).

    Main Methods:

    • A self-organizing recurrent radial basis function (SR-RBF) neural network is employed for nonlinear system prediction.
    • A spiking-based growing and pruning algorithm tunes the SR-RBF network structure.
    • An adaptive learning algorithm optimizes SR-RBF network parameters, coupled with an improved gradient method for NMPC optimization.
    • Lyapunov stability theory is used to prove the control system's stability.

    Main Results:

    • The SR-RBF neural network accurately predicts future dynamic behaviors of nonlinear systems.
    • The developed SR-RBF-NMPC achieves superior model fitting for wastewater treatment processes compared to existing methods.
    • The SR-RBF-NMPC demonstrates significantly improved control performance for dissolved oxygen concentration.

    Conclusions:

    • The proposed SR-RBF-NMPC offers a robust and adaptive control solution for complex nonlinear systems.
    • This approach provides enhanced modeling accuracy and control effectiveness in challenging environmental applications like WWTP.
    • The concurrent structure and parameter tuning of the SR-RBF network is key to its superior performance.