DocumentCode :
2768683
Title :
Robust adaptive decoupling design for generalized predictive control with neural network
Author :
Qin, Xiao F. ; Zhu, Kuan Y. ; Chai, Tian Y.
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Inst., Singapore
Volume :
3
fYear :
1996
fDate :
11-13 Dec 1996
Firstpage :
2426
Abstract :
A novel robust decoupling method with multivariable generalized predictive control (MGPC) for a class of nonlinear systems is presented in an adaptive version. System cross-coupling action and the nonlinear actors are identified online by a neural network, which is then compensated in the control algorithm using the feedforward technique to realize robust decoupling. The identified result is also taken as a modifying signal of the parameter estimate so that the equivalent model matches the real system well. Simulation results show the effectiveness of the proposed algorithm
Keywords :
adaptive control; compensation; control system synthesis; feedforward; multivariable control systems; neural nets; nonlinear control systems; parameter estimation; predictive control; robust control; feedforward technique; multivariable generalized predictive control; nonlinear actors; nonlinear systems; robust adaptive decoupling design; system cross-coupling action; Adaptive control; Control systems; Feedforward neural networks; Neural networks; Nonlinear control systems; Nonlinear systems; Predictive control; Programmable control; Robust control; Robustness;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control, 1996., Proceedings of the 35th IEEE Conference on
Conference_Location :
Kobe
ISSN :
0191-2216
Print_ISBN :
0-7803-3590-2
Type :
conf
DOI :
10.1109/CDC.1996.573453
Filename :
573453
Link To Document :
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