Enhancing trajectory tracking for a class of process control problems using iterative learning

A method of enhancing tracking in repetitive processes, which can be approximated by a first-order plus dead-time model is presented. Enhancement is achieved through filter-based iterative learning control (ILC). The design of the ILC parameters is conducted in frequency domain, which guarantees the convergence property in iteration domain. The filter-based ILC can be easily added to existing control systems. To clearly demonstrate the features of the proposed ILC, a water heating process under a PI controller is used as a testbed. The empirical results show improved tracking performance with iterative learning.

Authors

• Jian-xin Xu
• Tong-Heng Lee
• Ying Tan School of Intelligence Science and Technology, Peking University Key Laboratory of Machine Perceptron (MOE), Peking University Institute for Artificial Intelligence, Peking University National Key Laboratory of General Artificial Intelligence

Keywords

• Enhance tracking
• Filter-based iterative learning control
• Frequency convergence analysis