Neural network-based predictive control for multivariable processes

Chen, Junghui; Yea, Yuezhi

doi:10.1080/00986440213128

Cited by 10 publications

(7 citation statements)

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“…Such tasks are particularly difficult when additional constraints on the control signals are considered, which demands using some numerical procedures to solve optimization problems with constraints. When a nonlinear description of the plant is not known accurately, predictive controllers employing artificial intelligence, for example, neural networks (Akesson and Tojvonen, 2006;Ławryńczuk, 2010;Chen and Yea, 2002;van der Boom et al, 2005) or modern versatile neuro-approximators (Tzirkel-Hancock and Fallside, 1992;Fabri and Kadrikamanathan, 2001;Pedro and Dahunsi, 2011) can be used.…”

Section: S Bańka Et Almentioning

confidence: 99%

Design of a multivariable neural controller for control of a nonlinear MIMO plant

Bańka

Dworak

Jaroszewski

2014

International Journal of Applied Mathematics and Computer Science

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The paper presents the training problem of a set of neural nets to obtain a (gain-scheduling, adaptive) multivariable neural controller for control of a nonlinear MIMO dynamic process represented by a mathematical model of Low-Frequency (LF) motions of a drillship over the drilling point at the sea bottom. The designed neural controller contains a set of neural nets that determine values of its parameters chosen on the basis of two measured auxiliary signals. These are the ship's current forward speed measured with respect to water and the systematically calculated difference between the course angle and the sea current (yaw angle). Four different methods for synthesis of multivariable modal controllers are used to obtain source data for training the neural controller with parameters reproduced by neural networks. Neural networks are designed on the basis of 3650 modal controllers obtained with the use of the pole placement technique after having linearized the model of LF motions made by the vessel at its nominal operating points in steady states that are dependent on the specified yaw angle and the sea current velocity. The final part of the paper includes simulation results of system operation with a neural controller along with conclusions and final remarks.

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Section: S Bańka Et Almentioning

confidence: 99%

Design of a multivariable neural controller for control of a nonlinear MIMO plant

Bańka

Dworak

Jaroszewski

2014

International Journal of Applied Mathematics and Computer Science

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“…A few reported cases (e.g. Chen & Yea, 2002;Yu & Gomm, 2003) usually decompose the system into a number of multi-input, single-output (MISO) subsystems which are later combined to form a parallel process model. This leads to sub-optimal results.…”

Section: The Multistage Evaporator: System Descriptionmentioning

confidence: 99%

Identification and predictive control of a multistage evaporator

Atuonwu

Cao

Rangaiah

et al. 2010

Control Engineering Practice

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a b s t r a c tA recurrent neural network-based nonlinear model predictive control (NMPC) scheme in parallel with PI control loops is developed for a simulation model of an industrial-scale five-stage evaporator. Inputoutput data from system identification experiments are used in training the network using the Levenberg-Marquardt algorithm with automatic differentiation. The same optimization algorithm is used in predictive control of the plant. The scheme is tested with set-point tracking and disturbance rejection problems on the plant while control performance is compared with that of PI controllers, a simplified mechanistic model-based NMPC developed in previous work and a linear model predictive controller (LMPC). Results show significant improvements in control performance by the new parallel NMPC-PI control scheme.

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“…Currently, applications of single neural networks in process modeling and control are quite significant in industry especially in model based predictive control (MBPC) (e.g. Chen and Yea, 2002;Xiong and Jutan, 2002) and this is due to the ability of neural networks in modeling nonlinear processes (e.g. Shaw et al, 1997).…”

Section: Introductionmentioning

confidence: 99%

Prediction of equilibrium water dew point of natural gas in TEG dehydration systems using Bayesian Feedforward Artificial Neural Network (FANN)

Ahmad

Bahadori

Zhang

2018

Petroleum Science and Technology

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The aim of this paper is to predict the equilibrium water dew point of natural gas in TEG dehydration process using feedforward artificial neural network (FANN) and validation with the literatures values for generalization capability of the model. The FANN was trained by the Bayesian with the regularization method. The FANN consists of three layers which are input, hidden and output layer. The input layer is the manipulated variables which are contactor temperature in Kelvin and the TEG concentration in percent while the output layer is the equilibrium water dew point of the natural gas. The performances of the model were evaluated using the coefficient of determination, mean square error and average absolute deviation percentage. The predictive model shows a good result as the coefficient of determination of 0.9989 and 0.9976 was obtained for training and testing data respectively. Small value of mean square error of 0.0203 and 0.0221 was obtained for the training and testing data as well. 0.5 % of average absolute deviation percentage was observed which is comparable with the literatures. It clearly shows that FANN gives a good prediction on water dew point of natural gas in TEG dehydration process even though the process is highly dynamics.

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Neural network-based predictive control for multivariable processes

Cited by 10 publications

References 0 publications

Design of a multivariable neural controller for control of a nonlinear MIMO plant

Design of a multivariable neural controller for control of a nonlinear MIMO plant

Identification and predictive control of a multistage evaporator

Prediction of equilibrium water dew point of natural gas in TEG dehydration systems using Bayesian Feedforward Artificial Neural Network (FANN)

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