High-Performance Control for Precision Mechanical Motions

Abstract

Mechanical motion generation and vibration suppression is fundamental to modern machines and emerging innovations. Abilities to learn and compensate for complex mechanical system and disturbance dynamics are key to synthesizing adequate control actions to achieve precision motions. Using application case studies to motivate challenges and demonstrate implementation results, I will present control methods for addressing narrowband (repetitive control, iterative learning control) and broadband (adaptive control) motions and disturbances. I will attempt to convey a common theme, controller syntheses stemming from ideas of system dynamic inversions and utilizing solutions of optimal model matching problems.