This paper proposes an atomic force microscope (AFM) based force controller to push nanoparticles on the substrates since it is tedious for human. A block phase correlation-based algorithm is embedded into the controller for compensating the thermal drift during nanomanipulation. Further, a neural network (NN) is employed to approximate the unknown nanoparticle and substrate contact dynamics including the roughness effects. Using the NN-based adaptive force controller the task of pushing nanoparticles is demonstrated. Finally, using the Lyapunov-based stability analysis, the uniform ultimately boundedness (UUB) of the closed-loop signals is demonstrated
Q. Yang and J. Sarangapani, "Nanomanipulation Using Atomic Force Microscope with Drift Compensation," Proceedings of the American Control Conference, 2006, Institute of Electrical and Electronics Engineers (IEEE), Jun 2006.
The definitive version is available at https://doi.org/10.1109/ACC.2006.1655408
American Control Conference, 2006
Electrical and Computer Engineering
Keywords and Phrases
Lyapunov Methods; Adaptive Control; Atomic Force Microscopy; Closed Loop Systems; Compensation; Control System Analysis; Force Control; Manipulators; Neurocontrollers; Stability; Substrates
Article - Conference proceedings
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