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October 28, 2009

Mtg: Combining Motion Optimization and System ID for Robotics

by @ 2:25 pm. Filed under ALL, Computers/Software, Electronics Design, Engineering Mgmt, Semiconductors
 

WEDNESDAY November 18, 2009
SCV Control Systems Chapter
Speaker: Ghulam Mustafa, Ph.D., Crossing Automation
Time: Networking at 6:15 PM; Presentation at 6:30 PM
Cost: none
Place: Cogswell College, Board Room, 1175 Bordeaux Dr., Sunnyvale
RSVP: not required
Web: www.ewh.ieee.org/r6/scv/css

The speaker will present a novel approach that combines motion optimization and system identification via Taguchi-type design of experiments (DOE) and Monte Carlo simulation for quantifying the manufacturing variability of substrate-handling robots.? Motion control optimization refers to selecting a set of parameters that results in system responses that meets the performance specifications.? The parameters of a given controller can have opposing effects on the closed loop response of a system.? For example, the overshoot and damping behavior are often conflicting control objectives and can pose a challenge for adjusting the parameters of a PID controller.? The PID parameters are often determined using ad-hoc methods, such as those proposed by Ziegler and Nichols in the 1940s.? The presented statistical approach addresses this challenge and will cover in-situ tests developed for a robotic application, such as the step and frequency response to optimize PID parameters.? Furthermore, the test data are also used to identify system dynamics, which indicate that the robot performance depends on damping and stiffness properties that vary within the population of assembled robot mechanisms.? The identified system model is then used in conjunction with Taguchi DOE and Monte Carlo simulation with damping and friction as noise parameters.? This results in a statistical distribution of performance metrics, such as over-shoot and settling time, which in turn enables a methodical approach for controller tuning.

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