| Summary: | 碩士 === 國立臺灣科技大學 === 機械工程系 === 98 === This thesis aims to develop necessary techniques in an embedded control system by using the FPGA based platform. The study takes piezoelectric actuator as a control system example and investigates several issues for digital controller implementation. The applied controls include non model based design such as PID control and neural network control. Moreover, repetitive control, a model based design approach, is also implemented on the piezo actuated system for periodic signal tracking at 10 kHz and 100 kHz sampling rate, respectively. The mathematical model for repetitive controller design is obtained by using Auto-Regressive eXogenous (ARX) algorithm, a system identification technique, for the repetitive controller design. To compensate for the hysteresis effect in the piezo actuated system, an inverse hysteretic compensator is also applied to further improve the tracking performance. The digital control algorithms were realized using embedded hardware CompactRIO developed by National Instruments. The controller includes Real Time module, FPGA module, and DAQ module. We specifically develop the controller firmware under an FPGA environment using LabVIEW software language and obtain extreme fast sampling rate and better control performance than similar works in the literature. Because the FPGA controller is a fixed point processor, design issues such as finite word length precision, overflow, and program optimization should be carefully considered. After performing fixed point analysis, this research successfully implements the advanced controllers on the selected embedded control system. The experimental results also demonstrate that the applied neural network control and repetitive control achieve better control performance than traditional PID control.
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