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Advances in Tracking Control for Piezoelectric Actuators Using Fuzzy Logic and Hammerstein-Wiener Compensation

Cristian Napole, Óscar Barambones, Isidro Calvo, Mohamed Derbeli, Mohammed Yousri Silaa, Javier Velasco

2020Mathematics19 citationsDOIOpen Access PDF

Abstract

Piezoelectric actuators (PEA) are devices that are used for nano- microdisplacement due to their high precision, but one of the major issues is the non-linearity phenomena caused by the hysteresis effect, which diminishes the positioning performance. This study presents a novel control structure in order to reduce the hysteresis effect and increase the PEA performance by using a fuzzy logic control (FLC) combined with a Hammerstein–Wiener (HW) black-box mapping as a feedforward (FF) compensation. In this research, a proportional-integral-derivative (PID) was contrasted with an FLC. From this comparison, the most accurate was taken and tested with a complex structure with HW-FF to verify the accuracy with the increment of complexity. All of the structures were implemented in a dSpace platform to control a commercial Thorlabs PEA. The tests have shown that an FLC combined with HW was the most accurate, since the FF compensate the hysteresis and the FLC reduced the errors; the integral of the absolute error (IAE), the root-mean-square error (RMSE), and relative root-mean-square-error (RRMSE) for this case were reduced by several magnitude orders when compared to the feedback structures. As a conclusion, a complex structure with a novel combination of FLC and HW-FF provided an increment in the accuracy for a high-precision PEA.

Topics & Concepts

Control theory (sociology)Mean squared errorHysteresisActuatorPID controllerFeed forwardFuzzy logicCompensation (psychology)Root mean squareMathematicsTracking errorComputer scienceEngineeringControl engineeringControl (management)PhysicsArtificial intelligenceStatisticsTemperature controlQuantum mechanicsPsychoanalysisPsychologyElectrical engineeringPiezoelectric Actuators and ControlAeroelasticity and Vibration ControlIterative Learning Control Systems
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