A team of researchers from the University of Zielona Góra — Wojciech Paszke, Maciej Patan, Krzysztof Patan, Kamil Klimkowicz, and Huai Xiao — participated in the 22nd Polish Control Conference (PCC 2026), the leading national event in the field of control engineering. The conference gathered top experts from across Poland and abroad to discuss advances in automation, dynamic systems, and intelligent control technologies. A major highlight of PCC 2026 was a dedicated scientific session showcasing results from the Polish-Chinese research project SHENG 3, focused on iterative learning control and repetitive control for complex industrial systems. The session, titled: Iterative Learning and Repetitive Control for Industrial Systems was chaired by Prof. Krzysztof Patan (University of Zielona Góra).

Session Program

1. Data-Driven Set-Point Learning Control for Piezoelectric Positioning Systems with Unknown Dynamics Subject to Hysteresis Nonlinearity (Y. Feng, S. Hao, T. Liu, W. Paszke)

2. Iterative Learning Control of Airfoil using COMSOL (P. Balik, M. Patan)

3. Experience Transfer-Based Iterative Learning Control of Non-Affine Nonlinear Systems for Trajectory Switching (F. Chen, H. Tao, Z. Zhuang, C. Liu, W. Paszke, E. Rogers)

4. Prediction-Based Iterative Learning Control of a Thermophotovoltaic System under Distributed Data Exchange (K. Klimkowicz, M. Patan, K. Patan)

The session attracted strong interest from conference participants, emphasizing the growing relevance of data-driven and learning-based control strategies in industrial applications.

The SHENG 3 collaboration was further strengthened through dedicated project meetings held during the conference. These discussions involved key partners from Jiangnan University: Prof. Hongfeng Tao and  Dr. Zhihe Zhuang. Their participation reinforced the long-standing international cooperation between Polish and Chinese research teams in advanced control methodologies.

In addition to the SHENG 3 session, researchers from the University of Zielona Góra contributed to the conference’s thematic track on soft robotics. In the session Sensing and Control in Soft Robotics, Dr. Huai Xiao and Prof. Maciej Patan presented:

Physics-Informed Neural Networks for Pneumatic Soft Manipulators under Sparse End-effector Observations: A Latent-State Reconstruction Framework

The work introduces a novel PINN-based approach for reconstructing latent states in pneumatic soft manipulators, addressing challenges related to limited sensing and nonlinear system behavior.