Please use this identifier to cite or link to this item: http://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.
RMID: 0030006996
DOI: 10.1109/TIE.2014.2308161
Appears in Collections:Electrical and Electronic Engineering publications

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