• International Journal of Precision Engineering and Manufacturing
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• v.9 no.3
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• pp.40-45
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• 2008

This paper presents a linear positioning system and its control algorithm design with nano accuracy/resolution. The basic linear stage structure is driven by an ultrasonic motor and its displacement feedback is detected by a LDGI (Laser Diffraction Grating Interferometer), which can achieve nanometer resolution. Due to the friction driving property of the ultrasonic motor, the driving situation differs in various ranges along the travel. Experiments have been carried out in order to observe and realize the phenomena of the three main driving modes: AC mode (for mm motion), Gate mode (for ${\mu}m$ motion), and DC mode (for nm motion). A proposed FCMAC (Fuzzy Cerebella Model Articulation Controller) control algorithm is implemented for manipulating and predicting the velocity variation during the motion of each mode respectively. The PCbased integral positioning system is built up with a NI DAQ Device by a BCB (Borland $C^{++}$ Builder) program to accomplish the purpose of an intelligent nanopositioning control.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1186-1188
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• 1996

Neural networks and fuzzy systems have attracted the attention of many researehers recently. In general, neural networks are used to obtain information about systems from input/output observation and learning procedure. On the other hand, fuzzy systems use fuzzy rules to identify or control systems. In this paper we present a generalized FCMAC(Fuzzified Cerebellar Model Articulation Controller) networks, by integrating fuzzy systems with the CMAC(Cerebellar Model Articulation Controller) networks. We propose a direct adaptive controller design based on FCMAC(fuzzified CMAC) networks. Simulation results reveal that the proposed adaptive controller is practically feasible in nonlinear plant control.