GA-Based Practical Auto-Tuning Technique for Industrial Robot Controller with System Identification

Abstract

This paper presents a practical auto-tuning technique based on a genetic algorithm (GA) for servo controllers of multi-axis industrial robots. Compared to conventional manual tuning techniques, the auto-tuning technique can help save an engineers' time and the cost of controller tuning, reduce performance deviation among products, and achieve higher control performance. The technique consists of two main processes. One is an autonomous system identification process involving the use of actual motion profiles of a typical robot. The other is an autonomous control gain tuning process in the frequency and time domains involving the use of a genetic algorithm, which satisfies the required tuning specifications, e. g., control performance, execution time, stability, and practical applicability in industries. The proposed technique has been validated through experiments performed with a six-axis industrial robot.