Aquired Dataset for Adaptive Uncertainty Regulation in Interval Type-2 Fuzzy Servo Control

This dataset contains the experimental servo-response data used in the study "Soft-Disagreement-Based Adaptive Uncertainty Regulation for Fuzzy Servo Control." The data were acquired from a Delta ASDA-B2 400 W industrial servo drive controlled by a Raspberry Pi embedded platform under multiple operating conditions, including normal operation, heavy load, external disturbance, actuator saturation, oscillatory response, and nonlinear operating regions. The dataset includes the measured servo responses and the performance indicators extracted from each experiment, such as rise time, settling time, overshoot, steady-state error, integral absolute error (IAE), control effort energy, tracking-error standard deviation, and maximum control effort. These data were used to train and evaluate the Support Vector Machine (SVM) classifier and the Fuzzy C-Means (FCM) clustering algorithm, which together form the supervisory soft-disagreement framework for adaptive Footprint of Uncertainty (FOU) regulation in the proposed Interval Type-2 Takagi–Sugeno fuzzy controller. The dataset is provided to support the reproducibility of the experimental results reported in the associated publication and may be used for research and educational purposes with appropriate citation of the original article.