Abstract
Two 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 control approach utilizes a learning-based feedforward term to compensate for periodic dynamics and high-gain terms 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. Simulation results are provided to illustrate the efficacy and performance of the control strategies.
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Part of the paper has been published in the Proceedings of the 2002 ASME International Mechanical Engineering Congress and Exposition.
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Fang, Y., Feemster, M., Dawson, D. et al. Nonlinear control techniques for an atomic force microscope system. J. Control Theory Appl. 3, 85–92 (2005). https://doi.org/10.1007/s11768-005-0066-6
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DOI: https://doi.org/10.1007/s11768-005-0066-6