The system analysis 2 lab was designed to teach students how to download data from the control system on the AEV and convert that data into physical guidelines. Students used these physical guidelines to calculate the AEV’s performance characteristics such as distance and position. As the AEV is running, the controller is recording data about every 60 milliseconds using Electrically Erasable Programmable Read-Only Memory (EEPROM). The EEPROM records data such as time, current and voltage supplied to electric motors, and wheel count from the reflectance sensors. After an AEV run is complete, MATLAB is used to upload the data. MATLAB can also be used to convert the data to physical guidelines and performance characteristics. The recorded data and observations made by the group while the AEV is on the track can be used for design adjustments needed to improve the performance of the AEV.
After the data was uploaded, the students calculated quantities such as time and distance. In general, as the time increased, the distance and position did as well. This is due to the AEV moving along the track. Despite the increase in distance and position, the voltage (counts) stayed the same at roughly 559. The current tended to fluctuate as the data was recorded. There wasn’t a particular value that the current tended to average. As students calculated the values of current and voltage, the values tended to increase as the time and distance travelled by the AEV increased. When a graph of power vs. time was plotted, there were four natural breaks in the data. The graph started with a large spike, leveled off to a less drastic oscillating line, spiked again, before settling into another less drastic oscillating line. At each of these spots, a box was drawn to illustrate the natural breaks in the data.
System Analysis 2 was successful in providing the team with data measured using the automatic control system. It also gave the engineers equations to convert the data recorded to physical parameters, and helped the team use those parameters to calculate performance and efficiency comparisons. After tweaking the code to make sure that the AEV stopped where it was supposed to on the track, the engineers easily extracted the data recorded using the provided MATLAB program. Then, the team assembled an excel document to organize the data and make it simple to convert it into physical parameters, and power consumption. The layout of the excel document made it easy for the team to determine power and energy consumption.
Figure 1: Power vs. Time
This is the plot for the power usage over time for the duration of the program.
Figure 2: Phase Breakdown of Power vs. Time
This table is a plot of power vs. time using color coded boxes to outline the different phases in the code.
When a graph of power vs. time was plotted, there were four natural breaks in the data. The graph started with a large spike, leveled off to a less drastic oscillating line, spiked again, before settling into another less drastic oscillating line. At each of these spots, a box was drawn to illustrate the natural breaks in the data.
Code:
// Set vehicle to go forward
reverse(4);
// Set all motors to 20%, go until the vehicle reaches 13.5 feet.
motorSpeed(4, 20);
goToAbsolutePosition(332);
// Reverse vehicle
reverse(4);
//Set all motors to 30% for 1 seconds and stop
motorSpeed(4, 30);
goFor(1);
brake(4)
Team Meeting Notes:
Date: 25–February–2016
Time: 10:30am-12:30pm
Members Present: Kailee Gulbin (KG), Ben Reed (BR), Paul Conway (PC), Grant Miller (GM)
Topics Discussed: Lab 6 Post-Lab
Objectives:
Today’s main focus was meeting as a team to discuss how to prepare an Executive Summary for Lab 6 and update the u.osu.edu website.
To do:
-u.osu.edu (KG)
-Executive Summary (PC, KG, GM, BR)
Reflections:
-The u.osu page was not finished completely.
-The executive summary was not proof read, and the error/recommendations section was not finished.