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Nonlinear control structures based on embedded neural system models.

G Lightbody1, G W Irwin

  • 1Dept. of Electr. and Electron. Eng., Queen's Univ., Belfast.

IEEE Transactions on Neural Networks
|January 1, 1997
PubMed
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This study explores neural networks for modeling and controlling nonlinear systems. A novel approach enables effective adaptive control using neural networks, demonstrated on a chemical reactor.

Area of Science:

  • * Control Engineering
  • * Artificial Intelligence
  • * Nonlinear System Dynamics

Background:

  • * Traditional adaptive control methods struggle with complex nonlinear systems.
  • * Neural networks offer powerful function approximation capabilities for system modeling.
  • * Challenges exist in training neural networks within closed-loop control systems.

Purpose of the Study:

  • * To investigate the application of neural networks for nonlinear system modeling and adaptive control.
  • * To propose a novel neural network-based sensitivity modeling approach for improved controller training.
  • * To introduce a new nonlinear internal model control (IMC) strategy utilizing neural networks.

Main Methods:

  • * Utilized multilayer perceptrons for nonlinear plant modeling.

Related Experiment Videos

  • * Developed a direct model reference adaptive control strategy with feedforward neural networks.
  • * Implemented a novel neural network-based sensitivity modeling for error backpropagation.
  • * Proposed a nonlinear IMC strategy using neural models for adaptive linear controllers.

    • Main Results:

    • * Demonstrated the feasibility of neural networks for nonlinear system modeling.
    • * Successfully addressed challenges in training neural networks within closed-loop systems.
    • * Developed a novel nonlinear IMC strategy that simplifies linear control law design.
    • * Validated the proposed techniques using a continuous stirred tank reactor case study.

    Conclusions:

    • * Neural networks are effective tools for modeling and adaptive control of nonlinear systems.
    • * The proposed sensitivity modeling and nonlinear IMC strategies offer significant advantages.
    • * The techniques provide a robust framework for advanced adaptive control applications.