Sean was born and raised on the Island of Maui and graduated from H.P. Baldwin High School, Class of 2014. He attends the University of Portland and will be a senior in the fall. Sean is pursuing a Bachelor of Science degree in Electrical Engineering with a minor in Computer Science. His engineering skills began with his Intermediate and High School robotics clubs. Sean’s technical interests are in the fields of power and communication systems, and signal processing. After graduating, he plans on returning home to work on Maui. Sean enjoys playing basketball, coaching youth basketball, and playing video games with his friends and family.
Home Island: Maui
High School: H.P. Baldwin High School
Institution when accepted: University of Portland
Akamai Project: Designing an Encoder-Based Velocity Feedback System for the Keck Telescopes
Project Site: W.M. Keck Observatory
Mentors: Tomas Kraususki and Ben McCarney
W.M. Keck Observatory is currently working on a Telescope Control System Upgrade (TCSU) project that improves the telescope’s performance and reliability, while reducing maintenance needs and addressing serious obsolescence issues. Part of the original TCSU project was to replace the telescope’s motor tachometers that are used for velocity feedback. Replacement of the tachometers would be accomplished through calculating velocity from the new position encoders but this sub-project was de-scoped due to a time-budget. The purpose of this Akamai project is to restart the old TCSU tachometer replacement sub-project by developing a prototype encoder-based velocity feedback system that may be used to replace the current motor tachometers at some point in the future. The main approach to the project is to take position data from the new Heidenhain encoders, filter them to eliminate noise, and then differentiate them to derive noise-reduced velocity data. A lab setting was constructed using the same encoder devices that are currently implemented on the Keck telescopes. Real and generated encoder data was simulated in Matlab to test various filter designs though analysis of the filtered data’s standard deviation in order to determine which filter eliminated the most noise. Matlab simulations determined that the best filter design was a low-pass Hamming window filter with a sampling frequency of 1000 Hz, a cutoff frequency of 100 Hz and a down-sampling of 10 taps. The filter is used to provide the control system with noise-reduced position data that will be differentiated into velocity data to replace noisy and inaccurate tachometer velocity data. The increased accuracy of feedback velocity data in the control system leads to higher stability of the system meaning that the adjustments needed to correct the telescope’s position is reduced. It is recommended to test the Hamming window filter in the constructed lab setting to determine any hardware limitations of the Encoder Interface Box, the feasibility of the filter in the current control system and the performance of the new filter compared to the motor tachometers.