Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/137797
Title: Adaptive compensation for infinite number of actuator failures based on tuning function approach
Authors: Lai, Guanyu
Wen, Changyun
Liu, Zhi
Zhang, Yun
Chen, Philip C. L.
Xie, Shengli
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2017
Source: Lai, G., Wen, C., Liu, Z., Zhang, Y., Chen, P. C. L., & Xie, S. (2018). Adaptive compensation for infinite number of actuator failures based on tuning function approach. Automatica, 87, 365-374. doi:10.1016/j.automatica.2017.07.014
Journal: Automatica 
Abstract: In controlling nonlinear uncertain systems, compensating for infinite number of actuator failures/faults based on the well-known tuning function approach is an important, yet challenging problem in the field of adaptive control. In fact, it has been illustrated through simulation examples that instability is observed when an existing tuning function based scheme designed for compensating finite number of actuator failures is applied to an infinite number case. So far, there is still no solution to this problem. In this paper, we address this issue by proposing a novel adaptive scheme. Technically, our scheme is developed from a new piecewise Lyapunov function analysis, the parameter projection and a modified tuning function method. It is proved that all closed-loop signals are ensured bounded by the control scheme even there is a possibility that the actuator failures take place infinitely, provided that the minimum time interval between two successive failures is bounded below by any positive scalar. Moreover, the ultimate bound of tracking error can be reduced arbitrarily small even for relatively frequent failures. In addition, a guideline for improving transient performance in terms of L2-norm of tracking error is also established. Perfect asymptotic tracking is obtained when the total number of actuator failures becomes finite.
URI: https://hdl.handle.net/10356/137797
ISSN: 0005-1098
DOI: 10.1016/j.automatica.2017.07.014
Rights: © 2017 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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