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A fuzzy logic controller.

T Yamakawa1

  • 1Department of Computer Science and Control Engineering, Kyushu Institute of Technology, Fukuoka, Japan.

Journal of Biotechnology
|June 1, 1992
PubMed
Summary

This study introduces fuzzy logic control for complex bioreactor systems, offering a simplified approach to managing uncertainties in process kinetics. This method enhances control by mimicking human reasoning for better decision-making.

Area of Science:

  • Chemical Engineering
  • Control Systems Engineering
  • Artificial Intelligence

Background:

  • Reactor kinetics are complex and difficult to model with traditional mathematical equations, hindering effective control.
  • Traditional control technologies struggle with the inherent uncertainties and nonlinearities in complex systems like bioreactors.
  • Human expert knowledge is crucial but challenging to integrate into automated control systems.

Purpose of the Study:

  • To present a novel fuzzy sets method for handling uncertainties in expert knowledge acquisition.
  • To develop a fuzzy logic controller (FLC) for sophisticated control of complex bioreactor systems.
  • To demonstrate a control strategy that simplifies complex system management without advanced mathematics.

Main Methods:

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  • Utilizing fuzzy sets theory to manage uncertainty and facilitate knowledge acquisition from human experts.
  • Implementing fuzzy inference (approximate reasoning) for decision-making, mimicking human cognitive processes.
  • Constructing a fuzzy logic controller suitable for nonlinear, multivariable, and time-variant systems.

    • Main Results:

    • The fuzzy sets method effectively handles uncertainties inherent in expert knowledge.
    • The developed fuzzy logic controller provides sophisticated control for bioreactor systems.
    • The approach simplifies the control of complex, nonlinear systems, making it accessible without advanced mathematical expertise.

    Conclusions:

    • Fuzzy logic control offers a viable and accessible solution for managing uncertainties in complex bioreactor operations.
    • The proposed method enhances bioreactor control by leveraging approximate reasoning, similar to human decision-making.
    • This approach facilitates more sophisticated and robust control of nonlinear, multivariable, and time-variant systems.