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Robust predictive tracking control for a class of nonlinear systems
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Dinh, Quang Truong, Marco, James, Yoon, Jong Il and Ahn, Kyoung Kwan (2018) Robust predictive tracking control for a class of nonlinear systems. Mechatronics, 52 . pp. 135-149. doi:10.1016/j.mechatronics.2018.04.010 ISSN 0957-4158.
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Official URL: https://doi.org/10.1016/j.mechatronics.2018.04.010
Abstract
A robust predictive tracking control (RPTC) approach is developed in this paper to deal with a class of nonlinear SISO systems. To improve the control performance, the RPTC architecture mainly consists of a robust fuzzy PID (RFPID)-based control module and a robust PI grey model (RPIGM)-based prediction module. The RFPID functions as the main control unit to drive the system to desired goals. The control gains are online optimized by neural network-based fuzzy tuners. Meanwhile using grey and neural network theories, the RPIGM is designed with two tasks: to forecast the future system output which is fed to the RFPID to optimize the controller parameters ahead of time; and to estimate the impacts of noises and disturbances on the system performance in order to create properly a compensating control signal. Furthermore, a fuzzy grey cognitive map (FGCM)-based decision tool is built to regulate the RPIGM prediction step size to maximize the control efforts. Convergences of both the predictor and controller are theoretically guaranteed by Lyapunov stability conditions. The effectiveness of the proposed RPTC approach has been proved through real-time experiments on a nonlinear SISO system.
| Item Type: | Journal Article | ||||||||
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| Subjects: | T Technology > TJ Mechanical engineering and machinery | ||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||
| Library of Congress Subject Headings (LCSH): | Predictive control, Automatic control, Intelligent control systems, Fuzzy systems, Nonlinear systems | ||||||||
| Journal or Publication Title: | Mechatronics | ||||||||
| Publisher: | Pergamon Press | ||||||||
| ISSN: | 0957-4158 | ||||||||
| Official Date: | June 2018 | ||||||||
| Dates: |
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| Volume: | 52 | ||||||||
| Page Range: | pp. 135-149 | ||||||||
| DOI: | 10.1016/j.mechatronics.2018.04.010 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| Date of first compliant deposit: | 29 May 2018 | ||||||||
| Date of first compliant Open Access: | 29 May 2019 | ||||||||
| RIOXX Funder/Project Grant: |
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| Is Part Of: | This work was supported by: Innovate UK through the “Agile Power Management System (APMS)”, project number: 102437, in collaboration with the WMG Centre of High Value Manufacturing (HVM), Babcock and Potenzathe; the “Next-generation construction machinery component specialization complex development program”, project number A010600025, through the Ministry of Trade, Industry and Energy (MOTIE) and Korea Institute for Advancement of Technology (KIAT); and the University of Ulsan | ||||||||
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