Date of Award
Master of Science
Atomic force microscope (AFM), scanning tunnel microscope (STM), precision robotic manipulator, etc. have been used widely for various applications. These systems undoubtedly perform the task with micro-level accuracy of operations. Note that in these applications, the use of piezoelectric actuators (PEA) is ubiquitous. The desired high-resolution and better accuracy by such instruments/ systems can be easily achieved by the use of PEA owing to its desired properties such as high precision, quick response, better stability, high stiffness, etc. These properties make PEA the best choice in the applications which demand a micro-nano level of accuracy in the operations. Though PEA is suitable in critical applications and systems requiring high accuracy, PEA exhibits undesirable nonlinearities due to its hysteresis and creep behavior. These nonlinearities significantly bound the accuracy of operation by PEA and, consequently, the use of PEA. To tackle the effect of hysteresis, creep and other nonlinearities exhibited by PEA, modeling and control of PEA are essential. The model based control approaches are, specifically, effective in achieving the desired level of performance, especially in tracking applications. However, modeling accuracy and linearization losses are the factors responsible for the compromise in the desired performance.
Mayur Shivaji Patil
Patil, Mayur Shivaji, "Long short-term memory (LSTM) neural network-based system identication and augmented predictive control of piezoelectric actuators" (2020). Graduate Theses and Dissertations. 18199.
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