@inproceedings{004bffe788904c03ab2a8dadf31f8c66,
title = "Adaptive output feedback control for a multi-motor driving system with completely tracking errors constraint",
abstract = "This paper proposes an adaptive output feedback controller for the multi-motor driving system (MDS) to achieve the precision motion control with completely tracking errors constraint. By adopting a K-filter observer to estimate the unknown system states, a modified barrier Lyapunov function (MBLF) is integrated into the adaptive output feedback control to make all the tracking errors constrained within the prescribed bounds. Since the MBLF is suitable for both constrained and unconstrained conditions, it expands the application filed of the classical Lyapunov function. Moreover, minimize learning parameter technique is utilized into the adaptive law design, which improves the adaptive learning process greatly. The system stability is proven by Lyapunov theory. The simulations are conducted on a four-motor driving system to illustrate the efficiency of the proposed controller.",
keywords = "K-filter observer, MBLF, MDS, adaptive output feedback control",
author = "Minlin Wang and Xueming Dong and Xuemei Ren",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 33rd Chinese Control and Decision Conference, CCDC 2021 ; Conference date: 22-05-2021 Through 24-05-2021",
year = "2021",
doi = "10.1109/CCDC52312.2021.9601779",
language = "English",
series = "Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2672--2677",
booktitle = "Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021",
address = "United States",
}