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Nonlinear internal model control using neural networks: application to processes with delay and design issues.

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We present a neural internal model control design for stable systems with delays. This method trains only the inverse of the delayed model, simplifying control system design and enhancing stability.

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Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Process Control

Background:

  • Internal model control (IMC) is a widely used control strategy.
  • Systems with time delays present significant challenges for controller design.
  • Neural networks offer powerful tools for modeling complex dynamics.

Purpose of the Study:

  • To propose a design procedure for neural internal model control (NIMC) systems applicable to stable processes with time delays.
  • To demonstrate that the presence of delay does not increase the order of the inverse model required for control.
  • To ensure robust stability and flexible tuning of control dynamics.

Main Methods:

  • Designing a nonadaptive indirect control system using neural networks.
  • Training the inverse of the process model, excluding the delay component.
  • Cascading the inverse model with a rallying model to define regulation dynamics and ensure stability.
  • Emphasizing neural models affine in the control input for direct inverse derivation.

Main Results:

  • The design procedure necessitates training only the delay-free inverse of the model.
  • The order of the inverse model is not increased by the presence of delay.
  • The rallying model allows for independent tuning of dynamic behavior and stability robustness.
  • Simulated process control validates the proposed NIMC design for systems with and without delays.

Conclusions:

  • The proposed NIMC design procedure effectively handles stable processes with time delays.
  • The method simplifies controller design by focusing on the inverse of the delay-free model.
  • The approach offers robust stability and adaptable control dynamics through the rallying model.