Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/622366
Title: Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network
Authors: Hong-Gui Han;Lu Zhang;Ying Hou;Jun-Fei Qiao
subject: Dissolved oxygen (DO) concentration|nonlinear model predictive control (NMPC)|recurrent radial basis function (SR-RBF) neural networks|wastewater treatment process (WWTP).|self-organizing
Year: 2016
Publisher: IEEE
Abstract: A nonlinear model predictive control (NMPC) scheme is developed in this paper based on a self-organizing recurrent radial basis function (SR-RBF) neural network, whose structure and parameters are adjusted concurrently in the training process. The proposed SR-RBF neural network is represented in a general nonlinear form for predicting the future dynamic behaviors of nonlinear systems. To improve the modeling accuracy, a spiking-based growing and pruning algorithm and an adaptive learning algorithm are developed to tune the structure and parameters of the SR-RBF neural network, respectively. Meanwhile, for the control problem, an improved gradient method is utilized for the solution of the optimization problem in NMPC. The stability of the resulting control system is proved based on the Lyapunov stability theory. Finally, the proposed SR-RBF neural network-based NMPC (SR-RBF-NMPC) is used to control the dissolved oxygen (DO) concentration in a wastewater treatment process (WWTP). Comparisons with other existing methods demonstrate that the SR-RBF-NMPC can achieve a considerably better model fitting for WWTP and a better control performance for DO concentration.
URI: http://localhost/handle/Hannan/150271
http://localhost/handle/Hannan/622366
ISSN: 2162-237X
2162-2388
volume: 27
issue: 2
Appears in Collections:2016

Files in This Item:
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Title: Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network
Authors: Hong-Gui Han;Lu Zhang;Ying Hou;Jun-Fei Qiao
subject: Dissolved oxygen (DO) concentration|nonlinear model predictive control (NMPC)|recurrent radial basis function (SR-RBF) neural networks|wastewater treatment process (WWTP).|self-organizing
Year: 2016
Publisher: IEEE
Abstract: A nonlinear model predictive control (NMPC) scheme is developed in this paper based on a self-organizing recurrent radial basis function (SR-RBF) neural network, whose structure and parameters are adjusted concurrently in the training process. The proposed SR-RBF neural network is represented in a general nonlinear form for predicting the future dynamic behaviors of nonlinear systems. To improve the modeling accuracy, a spiking-based growing and pruning algorithm and an adaptive learning algorithm are developed to tune the structure and parameters of the SR-RBF neural network, respectively. Meanwhile, for the control problem, an improved gradient method is utilized for the solution of the optimization problem in NMPC. The stability of the resulting control system is proved based on the Lyapunov stability theory. Finally, the proposed SR-RBF neural network-based NMPC (SR-RBF-NMPC) is used to control the dissolved oxygen (DO) concentration in a wastewater treatment process (WWTP). Comparisons with other existing methods demonstrate that the SR-RBF-NMPC can achieve a considerably better model fitting for WWTP and a better control performance for DO concentration.
URI: http://localhost/handle/Hannan/150271
http://localhost/handle/Hannan/622366
ISSN: 2162-237X
2162-2388
volume: 27
issue: 2
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7226832.pdf3 MBAdobe PDFThumbnail
Preview File
Title: Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network
Authors: Hong-Gui Han;Lu Zhang;Ying Hou;Jun-Fei Qiao
subject: Dissolved oxygen (DO) concentration|nonlinear model predictive control (NMPC)|recurrent radial basis function (SR-RBF) neural networks|wastewater treatment process (WWTP).|self-organizing
Year: 2016
Publisher: IEEE
Abstract: A nonlinear model predictive control (NMPC) scheme is developed in this paper based on a self-organizing recurrent radial basis function (SR-RBF) neural network, whose structure and parameters are adjusted concurrently in the training process. The proposed SR-RBF neural network is represented in a general nonlinear form for predicting the future dynamic behaviors of nonlinear systems. To improve the modeling accuracy, a spiking-based growing and pruning algorithm and an adaptive learning algorithm are developed to tune the structure and parameters of the SR-RBF neural network, respectively. Meanwhile, for the control problem, an improved gradient method is utilized for the solution of the optimization problem in NMPC. The stability of the resulting control system is proved based on the Lyapunov stability theory. Finally, the proposed SR-RBF neural network-based NMPC (SR-RBF-NMPC) is used to control the dissolved oxygen (DO) concentration in a wastewater treatment process (WWTP). Comparisons with other existing methods demonstrate that the SR-RBF-NMPC can achieve a considerably better model fitting for WWTP and a better control performance for DO concentration.
URI: http://localhost/handle/Hannan/150271
http://localhost/handle/Hannan/622366
ISSN: 2162-237X
2162-2388
volume: 27
issue: 2
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7226832.pdf3 MBAdobe PDFThumbnail
Preview File