@inproceedings{6c154c6629ec45b0acd0a42094a1e114,
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} 2020 IEEE.; 9th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2020 ; Conference date: 20-11-2020 Through 22-11-2020",
year = "2020",
month = nov,
day = "20",
doi = "10.1109/DDCLS49620.2020.9275262",
language = "English",
series = "Proceedings of 2020 IEEE 9th Data Driven Control and Learning Systems Conference, DDCLS 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1182--1187",
editor = "Mingxuan Sun and Huaguang Zhang",
booktitle = "Proceedings of 2020 IEEE 9th Data Driven Control and Learning Systems Conference, DDCLS 2020",
address = "United States",
}