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

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 un-certainty. 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.