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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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Adaptive Practical Nonlinear Model Predictive Control for Echo State Network Models.

Bernardo Barancelli Schwedersky, Rodolfo Cesar Costa Flesch, Samuel Bahu Rovea

    IEEE Transactions on Neural Networks and Learning Systems
    |September 8, 2021
    PubMed
    Summary

    This study introduces an adaptive nonlinear model predictive control (NMPC) using an online echo state network (ESN) model. The adaptive NMPC system outperforms baseline methods, especially during process changes, and is computationally efficient.

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    Area of Science:

    • Control Systems Engineering
    • Machine Learning Applications
    • Process Control

    Background:

    • Nonlinear Model Predictive Control (NMPC) is crucial for complex systems.
    • Online process model estimation is challenging but necessary for adaptive control.
    • Echo State Networks (ESNs) offer a data-driven approach for system modeling.

    Purpose of the Study:

    • To develop an adaptive NMPC algorithm using an online ESN.
    • To improve control performance under changing process parameters.
    • To ensure computational feasibility for real-time applications.

    Main Methods:

    • Online estimation of ESN parameters with recursive least-squares and an adaptive forgetting factor.
    • Utilizing a linearized ESN for dynamic matrix computation.
    • Implementing and evaluating the controller on a benchmark conical tank system.

    Main Results:

    • The adaptive NMPC achieved comparable results to nonadaptive methods under nominal conditions.
    • The proposed controller significantly outperformed baseline algorithms when process parameters changed.
    • The adaptive NMPC demonstrated a tenfold reduction in computational time compared to baseline NMPC.

    Conclusions:

    • The proposed adaptive NMPC with an online ESN provides robust and efficient control.
    • This approach enables computationally affordable implementation of advanced adaptive NMPC.
    • The method is particularly effective in dynamic environments with varying process parameters.