Enhanced Auto-Tuning of Feedback Controllers with Aggressive Search Ensuring Stability and Its Application to Galvano Scanner

This paper presents a practical and high-performance auto-tuning method for feedback controllers in industrial servo systems aimed at achieving fast and precise positioning. Conventional auto-tuning approaches generally limit control performance by avoiding an aggressive parameter search for fear of instability of the feedback control system during auto-tuning. To overcome this issue, the proposed method employs a stable parameter search space that ensures specified stability margins and searches for optimal parameters within this space. The proposed method is established by combining it with a genetic algorithm-based parameter search as an example global optimization-based auto-tuning method, and is adopted for fast and precise positioning control of a galvano scanner. The results of auto-tuning experiments demonstrate the effectiveness of the proposed method compared to a conventional auto-tuning method that does not consider a stable parameter space.