Design of a feedforward controller for AFM nanopositioning based on neural network control theory

This paper presents design procedure for a neural feedforward controller which can be used as an atomic force microscope system. We have used a three layered feed forward neural network for designing Feedforward Controller with Plant Inverse Learning. The effectiveness and validity of the designed controller were investigated by computer simulation and results obtained are compared with other control methods, and show superior performance. Advantages of using proposed controller include increased bandwidth of operation and easy implementation in nanopositioning for the AFM.