Nonlinear Control Techniques for the Atomic Force Microscope System

In this paper, three nonlinear control techniques are proposed for an atomic force microscope system. Initially, a learning-based control algorithm is developed for the microcantilever–sample system that achieves asymptotic cantilever tip tracking for periodic trajectories. Specifically, the hybrid control approach utilizes a combination of a learning-based feedforward term to compensate for periodic dynamics while hign-gain terms are utilized to account for non-periodic dynamics. An adaptive control algorithm is then developed to achieve asymptotic cantilever tip tracking for bounded tip trajectories despite uncertainty throughout the system parameters. Lastly, a nonlinear controller coupled with a nonlinear observer is designed to provide for asymptotic tracking and interaction force identification unders a set of assumptions.Copyright © 2002 by ASME

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