TY - JOUR
T1 - Virtual unmodeled dynamics modeling for nonlinear multivariable adaptive control with decoupling design
AU - Zhang, Yajun
AU - Chai, Tianyou
AU - Wang, Dianhui
AU - Chen, Xinkai
N1 - Funding Information:
Manuscript received June 5, 2016; accepted August 9, 2016. Date of publication September 19, 2016; date of current version February 14, 2018. This work was supported in part by the Natural Science Foundation of China under Grant 61403071 and 61603168, in part by the China PostDoctoral Science Foundation funded project under Grant 2014M561246, in part by the Fundamental Research Funds for the seed foundation under Grant N140804001, in part by the Natural Science Foundation of Liaoning Province under Grant 2015020144, in part by the National Natural Science under Grant 61590922, and in part by the Projects of Liaoning Province under Grant 2014020021 and Grant LR2015021. This paper was recommended by Associate Editor Y.-J. Liu.
Funding Information:
He is a Chair Professor with Northeastern University. He is the Director of the State Key Laboratory of Synthetical Automation for Process Industries, the National Engineering and Technology Research Center of Metallurgical Automation, and the Department of Information and Science of National Natural Science Foundation of China. His current research interests include adaptive control, intelligent decoupling control, integrated plant control and system, and the development of control technologies with applications to various industrial processes.
PY - 2018/3
Y1 - 2018/3
N2 - For a class of complex industrial processes with nonlinear, strongly coupled multivariable properties, a new multivariable decoupling design framework which based on the concepts of virtual unmodeled dynamics (VUD) and lower order linear models is proposed in this paper. First, a selftuning multivariable decoupling controller is constructed based on a lower order model. Then based on the compensator of the VUD, a nonlinear multivariable decoupling controller is designed, where a decomposition estimation algorithm is employed for modeling the VUD. In our proposed scheme, it solves the problem that the current input signal is embedded in the VUD and the true input data vector used by the learner model is difficult to be obtained in time. The linear and nonlinear decoupling controllers are integrated by an adaptive switching control algorithm to take advantage of their complementary features. Finally, the stability and convergence of the proposed algorithm is analyzed. Experimental tests on a heavily coupled nonlinear twin-tank system are carried out to demonstrate the effectiveness and the practicability of the proposed method.
AB - For a class of complex industrial processes with nonlinear, strongly coupled multivariable properties, a new multivariable decoupling design framework which based on the concepts of virtual unmodeled dynamics (VUD) and lower order linear models is proposed in this paper. First, a selftuning multivariable decoupling controller is constructed based on a lower order model. Then based on the compensator of the VUD, a nonlinear multivariable decoupling controller is designed, where a decomposition estimation algorithm is employed for modeling the VUD. In our proposed scheme, it solves the problem that the current input signal is embedded in the VUD and the true input data vector used by the learner model is difficult to be obtained in time. The linear and nonlinear decoupling controllers are integrated by an adaptive switching control algorithm to take advantage of their complementary features. Finally, the stability and convergence of the proposed algorithm is analyzed. Experimental tests on a heavily coupled nonlinear twin-tank system are carried out to demonstrate the effectiveness and the practicability of the proposed method.
KW - Adaptive neural fuzzy inference system (ANFIS)-based data modeling
KW - Decoupling
KW - Multivariable and nonlinear systems
KW - Switching control
KW - Virtual unmodeled dynamics (VUD)
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U2 - 10.1109/TSMC.2016.2602826
DO - 10.1109/TSMC.2016.2602826
M3 - Article
AN - SCOPUS:85058494076
SN - 2168-2216
VL - 48
SP - 342
EP - 353
JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems
JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems
IS - 3
M1 - 7571110
ER -