A Comparison of Three Nonlinear Controller Designs Applied to a Non-Adiabatic First-Order Exothermic Reaction in a CSTR

Piovoso, Michael J.; Kosanovich, Karlene A.; Rokhlenko, Vadim; Guez, A.

doi:10.23919/acc.1992.4792114

Cited by 21 publications

(11 citation statements)

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“…Pure linear The output of the neural network model is x D , the distillate output composition. The relationship between s 1 , the output of the first layer, and the input variables is given by (1) …”

Section: Neural Network Developmentmentioning

confidence: 99%

“…A number of applications of NNs to process control problems have been reported. Piovoso et al [1] have compared NN to other modeling approaches for Internal Model Control (IMC), global linearization, and generic model Control. Seaborg and co-workers have used radial basis function NN for nonlinear control and they have applied their approaches to simulated systems [2,3].…”

Section: Introductionmentioning

confidence: 99%

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Application of Feed Forward and Recurrent Neural Network Topologies for the Modeling and Identification of Binary Distillation Column

Singh¹,

Tyagi²,

Kumar³

2013

IETE J Res

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This paper presents identification of artificial neural network model of a Binary Distillation Column (BDC). In this paper, the two most common topologies of artificial neural networks in the area of control are introduced: Feed forward neural network and recurrent neural networks. The training of neural network has been performed by the data set acquired from real 9-tray continuous BDC setup available in laboratory. The network model is composed of two layers. A hyperbolic tangent sigmoid function and a pure linear function have been utilized as activation functions in the first and the second layers, respectively. The developed neural network model has been validated by an extensive data set of practical data received from real BDC setup.

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Section: Neural Network Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Feed Forward and Recurrent Neural Network Topologies for the Modeling and Identification of Binary Distillation Column

Singh¹,

Tyagi²,

Kumar³

2013

IETE J Res

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show abstract

“…NN controllers give excellent performance in the case of severe process/model mismatch [20]. These types of controllers have been implemented successfully for both modeling and control of nonlinear systems [21].…”

Section: Neural Network Model Predictive Control Algorithmmentioning

confidence: 99%

Neural-Networks-Based Feedback Linearization versus Model Predictive Control of Continuous Alcoholic Fermentation Process

Mjalli

Al‐Asheh

2005

Chem. Eng. Technol.

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In this work advanced nonlinear neural networks based control system design algorithms are adopted to control a mechanistic model for an ethanol fermentation process. The process model equations for such systems are highly nonlinear. A neural network strategy has been implemented in this work for capturing the dynamics of the mechanistic model for the fermentation process. The neural network achieved has been validated against the mechanistic model. Two neural network based nonlinear control strategies have also been adopted using the model identified. The performance of the feedback linearization technique was compared to neural network model predictive control in terms of stability and set point tracking capabilities. Under servo conditions, the feedback linearization algorithm gave comparable tracking and stability. The feedback linearization controller achieved the control target faster than the model predictive one but with vigorous and sudden controller moves.

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“…In the present study, an ANN dynamic modeling strategy of the drug release process is adopted due to the high nonlinearity and its noisy response of the processes. The validated ANN model is then used to train an ANN-based model predictive controller as previously described [21][22][23][24][25][26][27]. The parameters of the controller are later tuned to achieve good reference signal tracking.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the use of ultrasound to deliver chemotherapeutic agents to cancer cells from polymeric micelles

Husseini

Jabbar

Mjalli

et al. 2011

Journal of the Franklin Institute

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A Comparison of Three Nonlinear Controller Designs Applied to a Non-Adiabatic First-Order Exothermic Reaction in a CSTR

Cited by 21 publications

References 0 publications

Application of Feed Forward and Recurrent Neural Network Topologies for the Modeling and Identification of Binary Distillation Column

Application of Feed Forward and Recurrent Neural Network Topologies for the Modeling and Identification of Binary Distillation Column

Neural-Networks-Based Feedback Linearization versus Model Predictive Control of Continuous Alcoholic Fermentation Process

Optimizing the use of ultrasound to deliver chemotherapeutic agents to cancer cells from polymeric micelles

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