Monte Carlo ITAE Dispersion for Three Motor Control Algorithms

100 Monte Carlo simulations quantify the dispersion of the Integral Time Absolute Error metric for PI-MPCC, SMSC-MPCC, and MPDSC motor controllers under ±50% parameter mismatch. This 5.5 KB Excel dataset, authored by Magdy Meawad, provides a compact benchmark for control system robustness analysis.

Use Cases

• Compare the ITAE dispersion metric across PI-MPCC, SMSC-MPCC, and MPDSC controllers to identify the most robust algorithm under parameter mismatch.
• Analyze the distribution of Monte Carlo simulation results for each controller to assess performance variance.
• Use the parameter mismatch condition data to validate new control strategies against established benchmarks.

Strengths

• Contains results from 100 Monte Carlo simulation runs, providing a statistical basis for analysis.
• Directly compares three specific control algorithms: PI-MPCC, SMSC-MPCC, and MPDSC.
• Focuses on a clear, quantifiable performance metric: Integral Time Absolute Error dispersion.

Limitations

• Very small dataset at only 5.5 KB, offering limited scope for complex modeling.
• No raw column names or sample data are provided, obscuring the exact data structure.
• The dataset's focus is narrow, centered on a specific simulation experiment.

Provenance

Source

figshare

Collection Method

Monte Carlo simulation (N=100) of control systems under ±50% parameter mismatch.

Time Range

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Freshness

Last updated March 2026.

Geography

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