Experimental Data for Implementation-Aware Real-Time Benchmark of Incremental PI and Mamdani Fuzzy Logic Speed Control for a Brushed DC Motor

Description

This dataset contains the experimental data supporting the manuscript entitled Implementation-Aware Real-Time Benchmark of Incremental PI and Mamdani Fuzzy Logic Speed Control for a Brushed DC Motor. The experiments compare an incremental proportional–integral controller and a fixed-rule Mamdani Type-1 fuzzy logic controller implemented on the same Texas Instruments TMS320F2812 DSP platform. Both controllers were evaluated using identical encoder feedback, QEP-based speed estimation, a 1 ms sampling period, PWM actuation, voltage-command limitation, supply conditions, measured channels, and KPI-extraction procedures. The dataset covers nominally no-load step-response experiments at reference speeds of 955 rpm and 6685 rpm. The recorded quantities include reference speed, measured motor speed, voltage-equivalent command monitor, and armature current. The associated performance indicators include 10–90% rise time, percentage overshoot, ±1% settling time, transient peak armature current, and steady-state armature current. The files are organized by controller and operating point. A README file describes the file structure, variable names, units, controller parameters, and the procedure used to reproduce the reported performance indicators and figures.

Steps to reproduce

The dataset contains processed experimental response figures and supporting files for an incremental PI controller and a fixed-rule Mamdani Type-1 fuzzy logic controller. The experiments were performed at reference speeds of 955 rpm and 6685 rpm under nominally no-load conditions. Both controllers were implemented on a Texas Instruments TMS320F2812 DSP using QEP-based encoder feedback, a 1 ms sampling period, PWM actuation, and a 24 V supply. The response files present the measured speed and armature-current waveforms. The additional files contain the experimental setup, fuzzy membership functions, fuzzy control surface, and associated source code.

Institutions

• Universitat Politècnica de Catalunya

  Catalonia, Barcelona

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