Simulation Experiments (Part 3)

It’s time to continue my short series on simulation experiments. In Part 1 and Part 2 I discussed the basics of simulation experiments and general features of Experiment Manager, the SystemVision application for simplifying, executing, and managing simulation experiments. In this post I’ll use a simple motor driver example to illustrate the basics of Experiment Manager operation. Here is the circuit:

Setting up and running an experiment requires two types of system information: a list of parameters whose values can change during the experiment, and one or more saved measurements to execute for each experiment run. For my motor drive system, I want to look at two performance metrics related to the position of the motor shaft: risetime for the shaft to move to its final position, and the maximum of the shaft’s position. For system parameters, I will focus on parameters in the motor including the winding resistance, winding inductance,  torque constant, damping coefficient, and moment of inertia.

Too determine how much effect each motor parameter has on my measurements, I ran a simple sensitivity analysis. Based on my analysis setup, the risetime is most affected by the motor’s winding resistance and damping coefficient, and the damping coefficient has the largest effect on the shaft’s final position. So I setup my experiment to adjust values for the winding resistance and damping coefficient while measuring risetime and position.

Experiment Manager’s GUI-driven integration with Microsoft Excel allows me to quickly setup the experiment spreadsheet. The spreadsheet contains a single column for each parameter and measurement in my experiment. Once defined, here is what the spreadsheet looks like:

With the parameter and measurement columns defined, each row in the spreadsheet becomes an individual experiment. I define the values in the parameter columns, and SystemVision fills in the measurement columns with calculated information from the experiment. Here is what my spreadsheet looks like after running a series of experiments:

 Note the cell shading in the measurement columns. Experiment Manager allows me to set minimum and maximum limits for measurements, and then flags the measured data by coloring the cell based on whether the measurement is within, or outside of, measurement limits (e.g. in the above example, green indicates a passed measurment, oranage a failed measurement). Now I have two sets of complimentary data: waveforms in SystemVision which I can further analyze graphically, and numeric data in Excel that I can analyze using spreadsheet functions.

System experiments are an essential part of designing and verifying a mechatronic system design. But setting up experiments often requires significant time, particularly if it’s a manual process. Tools like Experiment Manager speed the experiment definition process and help design teams automate experiment setup and execution to make design flows more efficient and improve system quality.