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Related Concept Videos

Feedback control systems01:26

Feedback control systems

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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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Related Experiment Video

Updated: Apr 14, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
05:47

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

646

A Combined Adaptive Neural Network and Nonlinear Model Predictive Control for Multirate Networked Industrial Process

Tong Wang, Huijun Gao, Jianbin Qiu

    IEEE Transactions on Neural Networks and Learning Systems
    |April 22, 2015
    PubMed
    Summary

    This study introduces a novel multirate control strategy for industrial processes, enhancing stability and performance using adaptive neural networks (NN) and nonlinear model predictive control (NMPC) to manage network delays and data loss.

    Related Experiment Videos

    Last Updated: Apr 14, 2026

    Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
    05:47

    Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

    Published on: August 29, 2025

    646

    Area of Science:

    • Control Engineering
    • Industrial Process Control
    • Networked Systems

    Background:

    • Industrial processes often face challenges with multirate sampling and network-induced uncertainties.
    • Existing control methods struggle to effectively manage varying sampling periods and communication issues in complex systems.

    Purpose of the Study:

    • To develop a robust control strategy for double-layer architecture in multirate networked industrial processes.
    • To address output tracking, performance prediction, and system stability under network-induced delays and packet dropouts.

    Main Methods:

    • Adaptive neural network (NN) control for sampled-data nonlinear plants at the device layer with sampling period T(d).
    • Radial basis function (RBF) NN for predicting overall performance index at the operation layer using sampling period T(u).
    • Nonlinear model predictive control (NMPC) to ensure closed-loop system stability and compensate for network disturbances.

    Main Results:

    • Demonstrated successful output tracking of subsystems at the device layer.
    • Validated the prediction of the overall performance index using RBF NN.
    • Confirmed system stability and compensation for network delays and packet dropouts via NMPC.

    Conclusions:

    • The proposed multirate control strategy effectively handles complex networked industrial processes.
    • The combination of adaptive NN and NMPC provides robust control, ensuring system stability and performance.
    • Simulation results on a continuous stirred tank reactor system validate the method's practical applicability.