PERFORMANCE EVALUATION OF CONTROLLERS USING FUZZY DELPHI AND AHP TECHNIQUES

Abstract

This study introduces an enhanced decision-support framework that integrates the Fuzzy Delphi method with the Analytic Hierarchy Process (AHP) to improve controller tuning and performance evaluation in uncertain environments. Traditional tuning techniques typically depend on crisp expert judgments and deterministic performance indices; however, industrial control systems are characterized by nonlinear behaviors, uncertain parameter variations, and subjective expert evaluations that are often vague or inconsistent. The Fuzzy Delphi procedure is applied to systematically gather, filter, and consolidate expert insights, enabling a refined set of performance criteria such as stability margins, robustness to disturbances, settling time, overshoot, control effort, and energy consumption to be identified with greater precision despite linguistic ambiguity. These validated criteria are subsequently incorporated into an AHP hierarchy, where pairwise comparisons yield coherent, quantitative weights for multi-criteria assessment of controller alternatives. By merging the strengths of both methods, the proposed Fuzzy Delphi–AHP framework delivers a more rigorous and transparent basis for selecting tuning parameters and ranking controller designs. It improves the reliability of decision-making, reduces bias in expert-based evaluations, and facilitates a structured comparison among different control strategies.