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Output feedback neurolinearization.

R B Boozarjomehry1, W Y Svrcek

  • 1Chemical and Petroleum Engineering Department, The University of Calgary, Canada.

ISA Transactions
|May 23, 2001
PubMed
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A novel output-feedback neurolinearization method achieves model-independent input-output linearization. This advanced control strategy outperforms traditional methods in setpoint tracking and disturbance rejection for chemical processes.

Area of Science:

  • Chemical Engineering
  • Control Systems
  • Artificial Intelligence

Background:

  • Model-independent control is crucial for complex industrial processes.
  • Existing linearization techniques often require accurate process models.

Purpose of the Study:

  • To introduce and evaluate a new model-independent control method: output-feedback neurolinearization.
  • To compare its performance against Global Linearizing Control (GLC) and PI controllers.

Main Methods:

  • Developed an output-feedback neurolinearization technique utilizing only system input-output data.
  • Applied and compared the method to temperature control in a CSTR reactor and pH control in a neutralization process.

Main Results:

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  • Output-feedback neurolinearization demonstrated superior performance in setpoint tracking.
  • The method showed enhanced disturbance rejection capabilities compared to GLC and PI controllers.
  • The technique's model-independent nature was confirmed as a significant advantage.

    • Conclusions:

    • Output-feedback neurolinearization offers a robust and effective alternative for process control.
    • Its model-independent nature simplifies implementation and broadens applicability.
    • This method holds significant potential for improving control performance in various chemical engineering applications.