Data-driven frequency-domain iterative learning control with transfer learning
Year: 2025
Type: article
Abstract: Data-driven iterative learning control (ILC) can achieve improved tracking performance over model-based ILC by eliminating fitting error from parametric system representations. Existing data-driven approaches in frequency domain take advantage of the affordability and speed associated with acquiring non-parametric frequency response function data for effective learning. However, the quality of data significantly influences the achievable performance. Additionally, a notable drawback is that learning is reset whenever the tracked trajectory changes, despite having learned similar frequency contents before. Extending these approaches to multivariate systems with non-negligible coupling is also not straightforward. This paper aims to address the aforementioned challenges in data-driven ILCby employing spectral analysis (SPA), which improves the learned data-driven inversion by mitigating the measurement noise. Fast and robust convergence is made possible through an iteration-varying learning gain. Also proposed is a transfer learning strategy in the frequency domain, wherein the inversion learned in specific frequency bin(s) will be preserved and utilized to expedite convergence in subsequent tasks. The presented ILC algorithm based on SPA naturally extends to the multi-input multi-output (MIMO) framework, and the convergence can be ensured by complex-valued matrix analysis. The methodology is experimentally validated on a galvanometer mirror steering system, showcasing enhanced performance, transfer learning capabilities, and applicability to MIMO systems.
Source: Mechatronics
Authors: Yu-Hsiu Lee, Yu-Hsiang Chin, Chun-Yuan Hsueh
Institution: National Taiwan University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,940
OpenAlex ID: W4409849032