Neural network sliding mode controller of atomic force microscope-based manipulation with different cantilever probes.

Development of nanotechnology has given rise to various applications, including the nano-manipulation process within small-size environments. The implementation of such processes requires the use of tools and proper equipment and understanding of various factors influencing it. One such tool is the atomic force microscope (AFM) and its probe, used for imaging surfaces and manipulation tools. The AFM probe is the most important element of the AFM with a key role in system function. The dynamic analysis and control of AFM are necessary to increase efficiency. In this paper, a model of AFM is reviewed and rewritten by considering various cantilever probes, including rectangular, V-shaped, and dagger. The AFM actuator was modeled and analyzed on uncertain conditions. The position of the stage was controlled to the desired position through the desired motion profiles. To overcome the problem of model nonlinearity, a neural network (NN) sliding mode controller was used to optimize the controller parameter and provide the desired output. The simulation of system was performed by the effective parameters, its control was implemented, and the results were analyzed. The simulation revealed that the modified sliding mode controller with learnable NN improved controller performance by decreasing the rise time and eliminating the overshot.