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https://hdl.handle.net/2440/85480
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Type: | Journal article |
Title: | A novel model-free adaptive control design for multivariable industrial processes |
Author: | Xu, D. Jiang, B. Shi, P. |
Citation: | IEEE Transactions on Industrial Electronics, 2014; 61(11):6391-6398 |
Publisher: | IEEE |
Issue Date: | 2014 |
ISSN: | 0278-0046 1557-9948 |
Statement of Responsibility: | Dezhi Xu, Bin Jiang and Peng Shi |
Abstract: | In this paper, a multiple adaptive observer-based strategy is proposed for the control of multi-input multi-output nonlinear processes using input/output (I/O) data. In the strategy, the pseudopartial-derivative parameter matrix of compact form dynamic linearization is estimated by a multiple adaptive observer, which is used to dynamically linearize a nonlinear system. Then, the proposed data-driven model-free-adaptive-control algorithm is only based on the online identified multiobserver models derived from the I/O data of the controlled plants, and Lyapunov-based stability analysis is used to ensure that all signals of the close-loop control system are bounded. A numerical example and a Wood/Berry distillation column example are provided to show that the proposed control algorithm has a very reliable tracking ability and a satisfactory robustness to disturbances and process dynamics variations. |
Keywords: | Data-driven control; model-free adaptive control (MFAC); multiple adaptive observer; multivariable nonlinear systems; pseudopartial derivative (PPD) |
Rights: | © 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. |
DOI: | 10.1109/TIE.2014.2308161 |
Published version: | http://dx.doi.org/10.1109/tie.2014.2308161 |
Appears in Collections: | Aurora harvest 2 Electrical and Electronic Engineering publications |
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