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Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
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RBFNN-Based Data-Driven Predictive Iterative Learning Control for Nonaffine Nonlinear Systems.

Qiongxia Yu, Zhongsheng Hou, Xuhui Bu

    IEEE Transactions on Neural Networks and Learning Systems
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    Summary
    This summary is machine-generated.

    This study introduces a data-driven predictive iterative learning control (DDPILC) scheme using radial basis function neural networks (RBFNNs) to manage complex nonlinear systems with disturbances. The novel approach ensures reduced modeling and tracking errors with increased operation cycles.

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence in Control
    • Nonlinear System Dynamics

    Background:

    • Controlling nonaffine nonlinear discrete-time systems with unknown parameters and disturbances is challenging.
    • Existing methods often struggle with nonrepetitive external disturbances and unknown system dynamics.

    Purpose of the Study:

    • To propose a novel data-driven predictive iterative learning control (DDPILC) scheme.
    • To address unknown system parameters and nonrepetitive external disturbances in nonlinear systems.
    • To guarantee the convergence of modeling and tracking control errors.

    Main Methods:

    • Utilized dynamic linearization technique (DLT) with a pseudopartial derivative (PPD).
    • Designed a radial basis function neural network (RBFNN) estimation algorithm for unknown PPD and disturbances.
    • Established a data-driven prediction model and analyzed convergence using a composite energy function (CEF).

    Main Results:

    • Guaranteed convergence of modeling error with tunable speed using the DLT-based RBFNN method.
    • Designed a DDPILC with a disturbance compensation term, analyzing tracking control error convergence.
    • Simulations demonstrated successive reduction in modeling and tracking errors with increasing operation number in a train system.

    Conclusions:

    • The proposed DDPILC scheme effectively handles nonaffine nonlinear discrete-time systems with unknown parameters and disturbances.
    • The RBFNN-based approach ensures robust control performance and error convergence.
    • The DDPILC scheme shows practical applicability, as validated by train operation system simulations.