Work

Iterative Learning Control with Adaptive Kalman Filtering for Trajectory Tracking in Non-Repetitive Time-Varying Systems

Year: 2025

Type: article

Abstract: This paper presents an adaptive Kalman filter (AKF)-enhanced iterative learning control (ILC) scheme to improve trajectory tracking in non-repetitive time-varying systems (NTVSs), particularly in industrial applications. Unlike traditional ILC methods that assume fixed system dynamics, gradual parameter variations in NTVSs require adaptive approaches to address factors such as tool wear and sensor drift, which significantly affect tracking accuracy. By integrating AKF, the proposed method continuously estimates time-varying parameters and uncertainties in real time, thus improving the robustness and adaptability of trajectory tracking. Theoretical analysis is conducted to confirm the robust convergence and stability of the AKF-enhanced ILC scheme under uncertain and time-varying conditions. Experimental results demonstrate that the proposed approach significantly outperforms conventional ILC methods, ensuring precise and reliable tracking performance in dynamic industrial scenarios.

Source: Axioms

DOI: 10.3390/axioms14050324

Authors: Lei Wang, Shunjie Zhu, Menghan Wei, Xiaoxiao Wang, Ziwei Huangfu, Yiyang Chen

Institutions: Wuxi University, Nanjing University of Information Science and Technology, Hong Kong Polytechnic University, Soochow University

Language: English

Topic: Iterative Learning Control Systems

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, Natural Science Foundation of Jiangsu Province