A Methodological Survey of Fuzzy Expert Systems in Industrial Process Control

International Journal of Computer Science and Technology Vol. 2, No. 2, February 2000 | pp. 9–16

DOI: 10.46882/2000/IJCST/000014

Review Article

Title: A Methodological Survey of Fuzzy Expert Systems in Industrial Process Control

Names of Authors: Jean-Pierre Dubois¹, Sanjay Mehta²

Authors’ Affiliations: ¹Institute of Automation, École Polytechnique Fédérale de Lausanne, Switzerland; ²Department of Electrical Engineering, Indian Institute of Technology, Bombay, India.

Abstract:
The application of classical linear control theory to complex industrial production systems often yields poor performance due to non-linearities, unmodeled dynamics, and unpredictable load disturbances. Fuzzy logic controllers have emerged as a resilient alternative, bridging human expert heuristic rules with computational execution loops. This paper presents a comprehensive methodological survey analyzing the design, implementation, and long-term stability parameters of fuzzy expert systems across diverse manufacturing plants. We synthesize empirical data from 92 foundational studies published across the past decade, contrasting Mamdani and Takagi-Sugeno inference models applied to chemical, metallurgical, and automotive assembly pipelines. The survey assesses criteria such as rule-base optimization, membership function tuning, computational runtime overhead, and fault tolerance boundaries under extreme environmental noise. The review reveals that while Takagi-Sugeno configurations consistently deliver superior mathematical precision in tracking high-speed mechanical trajectories, Mamdani architectures offer unmatched transparency and simpler debugging workflows for human operators. We summarize current barriers including rule-base explosion in multi-variable configurations and lack of formal mathematical proof structures for absolute stability, concluding with a standardized design template for hybrid fuzzy-proportional-integral-derivative (Fuzzy-PID) control systems.

Keywords: Fuzzy Logic Controllers, Industrial Automation, Process Engineering, Expert Systems, Rule Optimization, Comparative Review.

Manuscript Timeline: Received: November 12, 1999; Revised: December 20, 1999; Accepted: January 14, 2000; Published: February 25, 2000.

Citation: Dubois, J. P., & Mehta, S. (2000). A Methodological Survey of Fuzzy Expert Systems in Industrial Process Control. International Journal of Computer Science and Technology, 2(2), 9–16.

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