AI-based Controllers for a Z-Axis Micro Precision Positioning System

Abstract

Background: Stick-slip actuators are commonly used in Nano/Micro precision positioning systems, but their control is challenging due to factors like nonlinear friction, PEA hysteresis, and uncertainty. Researchers have made efforts to address these challenges and documented their findings in articles and patents. Methods: This study introduces a novel vertical stick-slip actuator and proposes two different methods for overcoming the challenges associated with controlling it. The first method involves training an inverse model of the actuator using a supervised machine-learning algorithm to determine the optimal number of signals and peak voltage required for the saw-tooth signals in an open-loop controller. The second method is a closed-loop controller that utilizes the maximum allowable peak voltage unless the positioning error is smaller than the maximum step size. At this point, the neural network-based controller adjusts the peak voltage to a lower value, ensuring that the actuator reaches the desired position at the end of the final signal. Results: According to the results, both controllers perform effectively. The open-loop and closed-loop controllers exhibit a relative error of 1.59% and 0.4%, respectively, for an arbitrary desired position in the final position. Conclusion: In conclusion, the suggested controllers offer a practical solution to the controlling challenges faced by stick-slip positioners, which are essential in the advancement of Nano/Micro sciences.