Selective Multi-Source Transfer Learning and Ensemble Learning for Piezoelectric Actuator Feedforward Control

Transfer learning enables the leveraging of knowledge acquired from other piezoelectric actuators (PEAs) to facilitate the positioning control of a target PEA. However, blind knowledge transfer from datasets irrelevant to the target PEA often leads to degraded displacement control performance. To address this challenge, this study proposes a transfer learning method, termed selective multi-source ensemble transfer learning (SMETL). The SMETL adopts a multi-source transfer learning framework integrated with Proxy A-distance (PAD)-based multi-source domain selection and a greedy ensemble transfer learning strategy. Only when the performance on the target domain validation is improved, fine-tuned GRU-CNN feedforward control models are screened into the ensemble. The outputs of the retained ensemble models are averaged to generate the final prediction. Comparative experiment results demonstrate that SMETL achieves superior control performance across all evaluation metrics. This confirms SMETL’s capability to effectively leverage multi-source domain knowledge and mitigate the risk of introducing irrelevant data.