Indirect adaptive structure for multivariable neural identification and control of a pilot distillation plant

Cañete, J. Fernández de; Saz-Orozco, P. del; García-Moral, I.; Gonzalez-Perez, S.

doi:10.1016/j.asoc.2012.03.062

Cited by 13 publications

(5 citation statements)

References 21 publications

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“…From the eqn (4), output generalized use ---------- (5) Where ---------- (6) and --- (7) Eqn (6) and Eqn (7) further used to minimize the cost function. The main objective is predicted output is close as possible to the set point.…”

Section: Model Predictive Controllermentioning

confidence: 99%

“…Actually the distillation column mathematical model needs to be implemented the predictive controller so that here the real time data will be taken from the distillation column and the model will be developed from the help of system identification technique. Fuzzy logic based model and control approach applied [6] and neural network employed to both the model and identification for distillation column [7]and it has been used to fuzzy-neural based inferential control [8] but all the scenarios did not provided any scope of optimization technique. S. Joe Qin has discussed about industries use of MPC [9].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Parameter Estimation and Predictive Control Design for a Nonlinear Reactive Distillation Column

Manimaran*,

Nagalakshmi,

Chitra

2019

IJRTE

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 Abstract: Reactive distillation column is one of the key elements in the process of Petroleum and chemical industries, which is having nonlinear, multivariable and non-stationary characteristics. The conventional controller like PID provides fruitless control action for nonlinear process. This paper deals the design of the various Model predictive controller algorithms to control composition and Temperature of the Reactive distillation column in biodiesel production. Here the Recursive Least square technique is used to estimate the parameters and build the exact model of the Process. The MATLAB policy is used and accomplished of the GPC, SMPC and PID.

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Section: Model Predictive Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parameter Estimation and Predictive Control Design for a Nonlinear Reactive Distillation Column

Manimaran*,

Nagalakshmi,

Chitra

2019

IJRTE

View full text Add to dashboard Cite

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“…The most popular methods for training are the gradient back propagation algorithm. Nevertheless, the problem with the gradient descent is in its slow convergence rate and in its inability to guarantee global minimum (Canete et al, 2012; Nasr and Chtourou, 2014). Some algorithms, which are fast and able to achieve a global minimum, lead to better identification performance; however, they require intensive computation and storage.…”

Section: Introductionmentioning

confidence: 99%

A novel neural emulator identification of nonlinear dynamical systems using Lyapunov stability theory

Hamza,

Zribi,

Farhat

2023

Transactions of the Institute of Measurement and Control

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This paper deals with a new weight-updating algorithm using Lyapunov stability theory (LST) for the training of a neural emulator (NE), of nonlinear systems, connected by an autonomous algorithm inspired from the real-time recurrent learning (RTRL). The proposed method is formulated by an inequality-constraint optimization problem where the Lagrange multiplier theory is used as the optimization tool. The contribution of this paper is the integration of the LST into the Lagrange constraint function to synthesize a new analytical adaptation gain rate satisfying the asymptotic stability of the NE and providing good emulation performances. To confirm the good performances and the convergence ability of the proposed adaptation algorithm, a numerical example and an experimental validation on a chemical reactor are proposed.

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“…Indirect adaptive controller that uses neural network was presented for the identification and control of experimental pilot distillation column. This system is multivariable with unknown dynamics and the neural network was trained using Levenberg-Marquardt algorithm [20]. Adaptive finite time stabilization of a class of switched nonlinear systems was investigated with unknown nonlinear terms using neural network.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems

Sakhre

Jain

Sapkal

et al. 2015

Computational Intelligence and Neuroscience

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Fuzzy Counter Propagation Neural Network (FCPN) controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL). FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN) which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN) and Back Propagation Network (BPN) on the basis of Mean Absolute Error (MAE), Mean Square Error (MSE), Best Fit Rate (BFR), and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO) and a single input and single output (SISO) gas furnace Box-Jenkins time series data.

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Indirect adaptive structure for multivariable neural identification and control of a pilot distillation plant

Cited by 13 publications

References 21 publications

Parameter Estimation and Predictive Control Design for a Nonlinear Reactive Distillation Column

Parameter Estimation and Predictive Control Design for a Nonlinear Reactive Distillation Column

A novel neural emulator identification of nonlinear dynamical systems using Lyapunov stability theory

Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems

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