Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Actuators

DOI: 10.3390/act15010045

Authors: Yaqian Hu, Herong Jin, Xiangcheng Chu, Ran Cao

Institutions: Yanshan University, State Key Laboratory of New Ceramics and Fine Processing, Tsinghua University

Language: English

Topic: Piezoelectric Actuators and Control

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funders: National Natural Science Foundation of China, Tsinghua University, Beijing Nova Program