Research

During my Ph.D. study, I focused on the design and analysis of iterative learning control algorithms for non-strictly repetitive scenarios. In particular, I worked on using the method of alternating projections to design optimization-based iterative learning control algorithms for systems with varying trial lengths. I have also worked on the direct data-based design of iterative learning control by introducing reinforcement learning.

If you are interested in any of the papers listed here, please feel free to send me an e-mail. I'm happy to discuss any topic!

Working Manuscripts

[W3] Data-enabled iterative learning control: A zero-sum game design for varying time-scale tasks
Zhihe Zhuang
Completed, September 2024

[W2] Minimum-energy iterative learning control for multi-agent systems with optimal intermediate-point allocation
Zhihe Zhuang, Chenhui Zhou, Luyuan Gao, Hongfeng Tao, Yiyang Chen, Wojciech Paszke, Vladimir Stojanovic
Submitted, August 2024

[W1] Constraint-aware ILC: A computationally efficient approach via alternating projections
Zhihe Zhuang, Max van Meer, Hongfeng Tao, Tom Oomen
Submitted, May 2024

Journal Publications

[J8] Non-lifted norm optimal iterative learning control for networked dynamical systems: A computationally efficient approach
Luyuan Gao, Zhihe Zhuang, Hongfeng Tao, Yiyang Chen, Vladimir Stojanovic
Journal of the Franklin Institute, 2024

[J7] Quantized iterative learning control of communication-constrained systems with encoding and decoding mechanism
Yujuan Tao, Hongfeng Tao, Zhihe Zhuang, Vladimir Stojanovic, Wojciech Paszke
Transactions of the Institute of Measurement and Control, 2023

[J6] Q-learning based fault estimation and fault tolerant iterative learning control for MIMO systems
Rui Wang, Zhihe Zhuang, Hongfeng Tao, Wojciech Paszke, Vladimir Stojanovic
ISA transactions, 2023

[J5] Alternating projection-based iterative learning control for discrete-time systems with non-uniform trial lengths
Zhihe Zhuang, Hongfeng Tao, Yiyang Chen, Eric Rogers, Tom Oomen, Wojciech Paszke
International Journal of Robust and Nonlinear Control, 2023

[J4] Optimal iterative learning control design for continuous-time systems with nonidentical trial lengths using alternating projections between multiple sets
Zhihe Zhuang, Hongfeng Tao, Yiyang Chen, Tom Oomen, Wojciech Paszke, Eric Rogers
Journal of the Franklin Institute, 2023

[J3] Feedback-aided PD-type iterative learning control for time-varying systems with non-uniform trial lengths
Shanglei Guan, Zhihe Zhuang, Hongfeng Tao, Yiyang Chen, Vladimir Stojanovic, Wojciech Paszke
Transactions of the Institute of Measurement and Control, 2022

[J2] An optimal iterative learning control approach for linear systems with nonuniform trial lengths under input constraints
Zhihe Zhuang, Hongfeng Tao, Yiyang Chen, Vladimir Stojanovic, Wojciech Paszke
IEEE Transactions on Systems, Man and Cybernetics: Systems, 2022

[J1] Iterative learning control for repetitive tasks with randomly varying trial lengths using successive projection
Zhihe Zhuang, Hongfeng Tao, Yiyang Chen, Vladimir Stojanovic, Wojciech Paszke
International Journal of Adaptive Control and Signal Processing, 2022

Peer-reviewed Conference Publications

[C5] Design of indirect-type iterative learning control for continuous-time batch processes with the repetitive process setting
Robert Maniarski, Wojciech Paszke, Hongfeng Tao, Zhihe Zhuang
14th Asian Control Conference (ASCC), Dalian, China, July 2024

[C4] Projection-based iterative learning control for linear systems with flexible tasks
Luyuan Gao, Zhihe Zhuang, Shanglei Guan, Hongfeng Tao, Jier Qiu, Wojciech Paszke
39th Youth Academic Annual Conference of Chinese Association of Automation (YAC), Dalian, China, June 2024

[C3] A norm optimal iterative learning control approach with efficient computation
Luyuan Gao, Zhihe Zhuang, Hongfeng Tao, Yiyang Chen, Wojciech Paszke
IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS), Kaifeng, China, May 2024

[C2] An optimal iterative learning control design framework for systems with varying trial lengths
Shanglei Guan, Zhihe Zhuang, Hongfeng Tao
China Automation Congress (CAC), Beijing, China, October 2021

[C1] Optimal iterative learning control of quantized signals based on encoding-decoding method
Yande Huang, Zhihe Zhuang, Hongfeng Tao, Yiyang Chen
IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS), Suzhou, China, May 2021

Non-peer-reviewed Presentations and Posters

[PP1] Alternating projection-based optimal ILC for linear systems with non-uniform trial lengths under input constraints
Zhihe Zhuang, Max van Meer, Hongfeng Tao, Dianqing Zhou, Tom Oomen
Oral presentation at the 42nd Benelux meeting on Systems and Control, Elspeet, the Netherlands, March 21-23rd, 2023

Journal Review

  • Automatica
  • ISA Transactions
  • Systems Science & Control Engineering

Conference Review

  • IEEE Data Driven Control and Learning Systems Conference (DDCLS)
  • IEEE/ASME Conference on Mechatronic, Embedded Systems and Applications (MESA)