Identification of twin-tanks dynamics using adaptive wavelet differential neural networks

Jahangiri, Fatemeh; Doustmohammadi, Ali; Menhaj, Mohammad Bagher

doi:10.1109/ijcnn.2010.5596343

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…A novel approach is employed in this work using a wavelet as the activation function in the neural network to model the QTP due to its inherent capability of both time and frequency signal localisation, which ultimately helps in achieving a global minimum solution. Wavelets are one of the most exciting research areas in signal processing today and researchers have increasingly seized the opportunity to employ wavelet functions with its choice of different mother wavelet in various modelling disciplines (Maalla et al 2008;Huang et al 2002;Lin et al 2003;Jajangiri et al 2010;Lu et al 2005;Meng & Sun 2008;Kuraz 2006;Lilong et al 2011;Coca & Billings 2012;Oussar et al 1998). Conventional approaches of training the NN structures such as the back propagation methods have been extensively used.…”

Section: Introductionmentioning

confidence: 99%

A non-minimum phase robust nonlinear neuro-wavelet predictive control strategy for a quadruple tank process

Owa

Khan

Sharma

et al. 2018

IJPSE

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In process industries model-plant mismatch is a significant problem. Quadruple Tank Process (QTP) can be configured both in minimum phase and non-minimum phase (NMP). However, in NMP, the control of QTP poses a challenge. This paper addresses that and presents a novel robust wavelet based non-minimum phase control (NMPC) strategy for the challenging QTP using genetic algorithm to find the optimised value of the manipulated variables in NMPC at every sampling time. The QTP is modelled based on wavelet neural network. The simulation results indicate that significant improvements have been achieved both in modelling and control strategies for a QTP system compare to conventional approaches such as the Levenberg-Marquardt Keywords: Wavelet Neural Network (WNN); right hand plane zero (RHPZ); non-minimum-phase (NMP), Non-linear Model Predictive Control (NMPC); quadruple-tank process (QTP) Genetic Algorithms (GA); Multi Input Multi Output (MIMO); model-plant mismatch (MPM); Non-linear Optimisation; Coupled Tank System (CTS).

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Section: Introductionmentioning

confidence: 99%

A non-minimum phase robust nonlinear neuro-wavelet predictive control strategy for a quadruple tank process

Owa

Khan

Sharma

et al. 2018

IJPSE

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“…29 Researchers have increasingly seized the opportunity to employ wavelet functions with its choice of different mother wavelets in various modelling disciplines and tasks. [29][30][31] Some of the widely used mother wavelets such as Morlet, Haar, Shannon, Mexican hat and Daubechies are chosen based on their diverse features. The work of Jahangiri et al 31 was based on Mexican hat mother wavelet and was able to establish that neurons activated by wavelet functions in the ANN model are more effective than the sigmoid functions when modelling the single-input single-output (SISO) CTS.…”

Section: Introductionmentioning

confidence: 99%

A novel real-time non-linear wavelet-based model predictive controller for a coupled tank system

Owa

Sharma

Sutton

2014

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article presents the design, simulation and real-time implementation of a constrained non-linear model predictive controller for a coupled tank system. A novel wavelet-based function neural network model and a genetic algorithm online non-linear real-time optimisation approach were used in the non-linear model predictive controller strategy. A coupled tank system, which resembles operations in many chemical processes, is complex and has inherent non-linearity, and hence, controlling such system is a challenging task. Particularly important is low-level control where often instability and oscillatory responses are observed. This article designs a wavelet neural network with high predicting precision and time-frequency localisation characteristics for an online prediction model in the non-linear model predictive controller to show the effectiveness of this approach in controlling the liquid at low level. To speed up the training process, a fast global search stochastic non-linear conjugate wavelet gradient algorithm is initially used to train the wavelet neural network structure before the genetic algorithm optimisation technique is utilised to tune adaptively the wavelet neural network parameters. The non-linear model predictive controller algorithm is tested for both approaches: first, in a simulation using identified models, and second, in a real-time practical application to a single-input single-output system coupled tank system. The results show an excellent control performance with respect to mean square error and average control energy values obtained.

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A nonlinear system identification approach based on Fuzzy Wavelet Neural Network

Linhares

Junior

Araújo

et al. 2015

Journal of Intelligent &Amp; Fuzzy Systems

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Identification of twin-tanks dynamics using adaptive wavelet differential neural networks

Cited by 3 publications

References 14 publications

A non-minimum phase robust nonlinear neuro-wavelet predictive control strategy for a quadruple tank process

A non-minimum phase robust nonlinear neuro-wavelet predictive control strategy for a quadruple tank process

A novel real-time non-linear wavelet-based model predictive controller for a coupled tank system

A nonlinear system identification approach based on Fuzzy Wavelet Neural Network

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