Abstract
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.
| Original language | English |
|---|---|
| Article number | 7571110 |
| Pages (from-to) | 342-353 |
| Number of pages | 12 |
| Journal | IEEE Transactions on Systems, Man, and Cybernetics: Systems |
| Volume | 48 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2018 Mar 1 |
Fingerprint
Keywords
- Adaptive neural fuzzy inference system (ANFIS)-based data modeling
- Decoupling
- Multivariable and nonlinear systems
- Switching control
- Virtual unmodeled dynamics (VUD)
ASJC Scopus subject areas
- Software
- Control and Systems Engineering
- Human-Computer Interaction
- Computer Science Applications
- Electrical and Electronic Engineering
Cite this
Virtual unmodeled dynamics modeling for nonlinear multivariable adaptive control with decoupling design. / Zhang, Yajun; Chai, Tianyou; Wang, Dianhui; Chen, Xinkai.
In: IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 48, No. 3, 7571110, 01.03.2018, p. 342-353.Research output: Contribution to journal › Article
}
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
PY - 2018/3/1
Y1 - 2018/3/1
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)
UR - http://www.scopus.com/inward/record.url?scp=85058494076&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85058494076&partnerID=8YFLogxK
U2 - 10.1109/TSMC.2016.2602826
DO - 10.1109/TSMC.2016.2602826
M3 - Article
AN - SCOPUS:85058494076
VL - 48
SP - 342
EP - 353
JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems
JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems
SN - 2168-2216
IS - 3
M1 - 7571110
ER -