.Credibility.Examples.SimpleControlledDriveNonlinear.SimpleControlledDrive_forCalibration

Information

A controlled simple drive train to be used for calibration, see also User's Guide. It consists of an excitation block sine that generates a sine sweep signal with constant amplitude and increasing frequency as reference value for the feedback controller speedController. The controller commands a motor torque for the drive train. Two sensors measure the angular velocities of the motor and load inertias. Measurement data, generated in a preprocessing step by means of a virtual test rig, is provided as table measurementsTable.

Calibration setup

The calibration criteria itself are not defined here and shall be composed by the user for the optimization-based calibration. As criteria input, the differences between simulated and measured data of motor and load angular velocities can be utilized, calculated in diffSpeedMotor and diffSpeedLoad, respectively.

To further configure the calibration optimization setup, the predefined calibration data of the drive train parameters, stored directly in the record data, shall be widely used. Let us demonstrate this on the calibration of the damping parameter damper.d as follows.

The calibration optimization setup consists of the nominal model with a starting value for the damping parameter of 300 N·m·s/rad and an assumed possible range for the parameter of [1, 1000] N·m·s/rad. Then, the optimization tuner setting shall be

• tuner1.value = data.damping.calibration.start (= 300),
• tuner1.min = data.damping.calibration.lower (= 1),
• tuner1.max = data.damping.calibration.upper (= 1000).

After a successful optimization run, the resulting optimal parameter value shall be stored (manually) in data.damping.value, together with its assumed uncertainty.