Replication Data for: Increasing Dynamic Accuracy using Predictive Feedforward with Hybrid Modeling

<p>Experimental dataset for model identification and validation of feedforward control on a five-axis milling machine.</p>

<p>This dataset belongs to the Open Access publication "Increasing dynamic accuracy of machine tools using predictive feedforward optimization with hybrid modeling" (doi: <a href="https://doi.org/10.1016/j.rcim.2025.103137">10.1016/j.rcim.2025.103137</a>) A detailed description of the setup can be found in the publication.</p>

<p>Feedback controllers from frequency inverter:</p>

<ul>
<li> PI velocity controller with Kp = 0.035 [Nm/(rad/s)], Tn = 2.6 [ms].</li>
<li> P position controller with Kp = 35 [1/s].</li>
</ul>

<p>The dataset contains the following folders (Feel free to contact the dataset owner if you have any questions about the dataset.):</p>

<ul>
<li> Identification
<br> measurements for identification of discrepancy model with different constant velocities as reference, and velocity sweeps for identification in frequency domain.
</li>
<li> Validation\energy_consumption
<br> measured energy consumption on the test bench of different feedforward schemes, quantified by direct current (DC) power
</li>
<li> Validation\multi-axis butterfly contour
<br> multi-axis tracking experiments comparing different feedforward schemes, the used butterfly G-code is also provided
</li>
<li> Validation\sensitivity analysis
<br> sensitivity analysis of the proposed MPFFC scheme against parametric model uncertainty
</li>
<li> Validation\tracking single axis (const velocity)
<br> single-axis tracking experiments at (unseen) consant velocities
</li>
<li> Validation\tracking single axis (transient)
<br> single-axis tracking experiments demonstrating transient behavior
</li>
</ul>