Paper Info
Title
Adaptive predictive control with recurrent fuzzy neural network for industrial processes
Abstract
The paper proposes an adaptive fuzzy predictive control method. The proposed controller is based on the Generalized predictive control (GPC) algorithm, and a recurrent fuzzy neural network (RFNN) is used to approximate the unknown nonlinear plant. To provide good accuracy in identification of unknown model parameters, an online adaptive law is proposed to adapt the consequent part of the RFNN, and its antecedent part is adapted by back-propagation method. The stability of closed-loop control system is studied and proved via the Lyapunov stability theory. A nonlinear lab oratory-scale liquid-level process is used to validate and demonstrate the performance of the proposed control. The simulation results show that the proposed method has good performance and disturbance rejection capacity in industrial processes and outperforms the PID and the classical GPC controllers.
Year
DOI
Venue
2011
10.1109/ETFA.2011.6059066
Emerging Technologies & Factory Automation
Keywords
Field
DocType
Lyapunov methods,adaptive control,backpropagation,closed loop systems,fuzzy neural nets,nonlinear control systems,predictive control,process control,recurrent neural nets,stability,Lyapunov stability theory,adaptive fuzzy predictive control,adaptive predictive control,backpropagation method,closed-loop control system,generalized predictive control,identification,industrial processes,online adaptive law,recurrent fuzzy neural network,unknown model parameters,unknown nonlinear plant
Intelligent control,Control theory,Neuro-fuzzy,PID controller,Computer science,Control theory,Model predictive control,Control engineering,Adaptive neuro fuzzy inference system,Control system,Adaptive control
Conference
ISSN
ISBN
Citations 
1946-0740 E-ISBN : 978-1-4577-0016-3
978-1-4577-0016-3
2
PageRank 
References 
Authors
0.52
6
3
Name
Order
Citations
PageRank
Jérôme Mendes1446.53
Nuno Sousa220.52
Rui Araújo316418.93