Kevin Lomeli's portfolio includes projects involving mechanical engineering and control systems. One project involved implementing various control strategies on an unstable magnetic bearing system. The strategies included classical lead/lag compensators, state feedback with observers, and polynomial model matching. Another project involved using a Kalman filter for state estimation of a missile guidance system. Simulation results showed the estimated states converged to the actual states. A third project analyzed the underlying principles of biological motion, modeling organisms as multi-link oscillatory systems. It explored optimizing input power and shape to analyze efficient motion.

1. KEVIN LOMELI
Mechanical Engineer
Graduate
Portfolio
photos taken by Kevin Lomeli
all rights reserved

2. GRADdesign & control systems
An emphasis on
SCHOOL
Fall 2009 - Spring 2010

3. MAGNETIC BEARING
A variety of control strategies implemented on a
complex unstable system.

4. MAGNETIC BEARING
What is it?
Whereas a conventional metal bearing is limited by its physical properties,
relatively greater friction and heat generation, a magnetic bearing allows a
rod to rotate without friction for high speed applications.

5. MAGNETIC BEARING
The Linearized Multi-Input Multi-Output Model
Control Strategies Method
• Classical Methods: Lead/Lag Compensators • Real-time step response data collection
• SISO & MIMO State Feedback • Matlab sim’s & calc’s
+ Observer & Internal Model
• Polynomial Model Matching
• And much much more...

6. MAGNETIC BEARING
Classical Methods
Utilized Nyquist and sensitivity analysis. The lead/lag
compensators ascertained stability, but poor performance.
• Large steady state error
• Poor transient behavior

7. MAGNETIC BEARING
SISO & MIMO
These are the results for SISO state observer with an internal integrator and sinusoid
compensator only (Refer to report for others). This is an improvement in performance
over classical methods.

8. MAGNETIC BEARING
Polynomial Model Matching
This approach yielded the best results.
Minimized steady state error
and optimized transient
behavior.

9. KALMAN FILTERING
Missile Guidance State Estimation

10. KALMAN FILTERING
THE SYSTEM
INITIAL CONDITIONS
FILTERING
ALGORITHM
A SERIES OF TESTS WERE CONDUCTED TO VALIDATE THE ACCURACY OF THE
STATE ESTIMATION

11. KALMAN FILTERING
UPON MONTE CARLO SIMULATION
AND ERROR ANALYSIS,
THE ESTIMATED STATES CONVERGE
TO THE ACTUAL ONES.

12. BIOLOGICAL CONTROL
The underlying principles of nature’s motion

13. A fish, snake or a worm can be modeled as a multi
link oscillatory system..
Governing Dynamics Cost Function
Input Power Function
Shape Derivative Function

14. Input Power Optimization
A snake moves most efficiently this way, but it has to squirm a
whole lot more.

15. Shape Derivative Optimization
A snake’s body moves less, but requires more energy to
move at the same pace.