Optimal Gain Tuning of Servo Drive Motion Controllers through Artificial Intelligence

Abstract

Automation equipment is usually composed of a servo motor connected to various mechanisms The performance of the equipment is therefore closely related to the response of the servo motor Servo motors are controlled by motion controllers thus in order for the equipment to achieve optimal performance the motion controller gains must be tuned appropriately Previous research related to gain tuning is difficult to implement due to the inherent difficulty in system modeling and mathematical calculations and traditional tuning methods are time-consuming and tedious This thesis presents a gain tuning method “Smart Tuning” which combines artificial intelligence (LightGBM) with an optimization algorithm (PSO) The LightGBM model predicts the response characteristics of a servo system under different controller gains and the optimal controller gains can then be obtained through PSO A fitness function which is used to evaluate the motion controller performance is also formulated by combining several position response characteristics such as percentage of overshoot and settling time etc and each characteristic is normalized to standardize the different units and scales The experimental results show that appropriate controller gains can be obtained through the proposed tuning method

Date of Award	2020
Original language	English
Supervisor	Mi-Ching Tsai (Supervisor)