Direct and indirect model based control using artificial neural networks

1991 ◽  
Vol 30 (12) ◽  
pp. 2564-2573 ◽  
Author(s):  
Dimitris C. Psichogios ◽  
Lyle H. Ungar
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Model Based Control ◽  
Model Based ◽  
Artificial Neural

Elevating Model Predictive Control Using Feedforward Artificial Neural Networks: A Review

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Senthil Kumar Arumugasamy ◽  
Zainal Ahmad
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Model Based Control ◽  
Model Based ◽  
Non Linear ◽  
Artificial Neural ◽  
Neural Predictive Control

Process control in the field of chemical engineering has always been a challenging task for the chemical engineers. Hence, the majority of processes found in the chemical industries are non-linear and in these cases the performance of the linear models can be inadequate. Recently a promising alternative modelling technique, artificial neural networks (ANNs), has found numerous applications in representing non-linear functional relationships between variables. A feedforward multi-layered neural network is a highly connected set of elementary non-linear neurons. Model-based control techniques were developed to obtain tighter control. Many model-based control schemes have been proposed to incorporate a process model into a control system. Among them, model predictive control (MPC) is the most common scheme. MPC is a general and mathematically feasible scheme to integrate our knowledge about a target, process controller design and operation, which allows flexible and efficient exploitation of our understanding of a target, and thus produces optimal performance of a system under various constraints. The need to handle some difficult control problems has led us to use ANN in MPC and has recently attracted a great deal of attention. The efficacy of the neural predictive control with the ability to perform comparably to the non linear neural network strategy in both set point tracking and disturbance rejection proves to have less computation expense for the neural predictive control. The neural network model predictive control (NNMPC) method has less perturbations and oscillations when dealing with noise as compared to the PI controllers.

ARTIFICIAL NEURAL NETWORKS: PRINCIPLE AND APPLICATION TO MODEL BASED CONTROL OF DRYING SYSTEMS - A REVIEW

1998 ◽  
Vol 16 (6) ◽  
pp. 931-966 ◽  
Author(s):  
T. Ttayagarajan ◽  
M. Ponnavaikko ◽  
J. Shanmugam ◽  
R.C. Panda ◽  
PG. Rao
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Model Based Control ◽  
Model Based ◽  
Artificial Neural

A novel hybrid model-based MPPT algorithm based on artificial neural networks for photovoltaic applications

2017 ◽  
Author(s):  
Mahyar Khosravi ◽  
Saeed Heshmatian ◽  
Davood A. Khaburi ◽  
Cristian Garcia ◽  
Jose Rodriguez
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Hybrid Model ◽  
Model Based ◽  
Photovoltaic Applications ◽  
Artificial Neural

A new global version of M(3000)F2 prediction model based on artificial neural networks

2014 ◽  
Vol 53 (3) ◽  
pp. 371-386 ◽  
Author(s):  
S.I. Oronsaye ◽  
L.A. McKinnell ◽  
J.B. Habarulema
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Model ◽  
Model Based ◽  
Artificial Neural
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