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Wang, Q., Dai, W., Ma, X. and Yang, C. (2017) Multiple Models and Neural Networks Based Adaptive PID Decoupling Control of Mine Main Fan Switchover System. IET Control Theory & Applications, 12, 446-455.
https://doi.org/10.1049/iet-cta.2017.0701

has been cited by the following article:

• TITLE: Improving Local Priority Hysteresis Switching Logic Convergence

  AUTHORS: Mubarak K. H. Alhajri, Muhammad A. Alsubaie, Salem H. Salamah

  KEYWORDS: Adaptive Control, Hysteresis Constant, Convergence

  JOURNAL NAME: Intelligent Control and Automation, Vol.11 No.1, December 23, 2019

  ABSTRACT: Local priority hysteresis switching logic is associated with adaptive control convergence when using an infinite set of candidate parameters via constraints added to the switching scheme. In this paper, we reevaluate these constraints on the basis of the persistent excitation assumption. This makes room for the adaptive control to converge to its optimum, resulting in improved performance. Unconstrained local priority hysteresis switching logic is investigated, and global convergence conditions are proposed. This paper expands on the preliminary version of a conference paper [1] by adding numerical simulation examples to validate both the application and the advantage of the theory.