Nonlinear output feedback control of a flexible link using adaptive neural network: controller design

Kuo, Peien; Hosein, Aiblaastin; Farmanborda, Muster Satis

doi:10.1177/1077546312445497

Cited by 8 publications

(4 citation statements)

References 378 publications

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“…These models have been successfully used in modeling of several industrial applications, e.g. electrical drives (Balestrino et al., 2001), sticky control valves (Srinivasan et al., 2005), solid oxide fuel cells (Jurado, 2006), stretch reflex dynamics (Westwick and Kearney, 2001), and control design (Kuo et al., 2012).…”

Section: Introductionmentioning

confidence: 99%

Instrument variable method based on nonlinear transformed instruments for Hammerstein system identification

Saini

Dewan

2017

Journal of Vibration and Control

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An extended instrumental variable (EIV) method is considered for the stochastic Hammerstein system (ARMAX and general model structure). The EIV method provides consistent parameter estimates by eliminating noise-induced bias in the least square (LS) method. To estimate the parameters, the Hammerstein model is formulated using the bilinear parameterization. The bilinear model is identified by introducing the nonlinear instrumental variables obtained from transformed delayed outputs using nonlinear mapping and polynomial basis of delayed inputs. These instruments are analyzed in full generality by computing the bounds on expected relationship between instruments and noise for the general noise disturbance structure. Then, a specific case with hyperbolic tangent (tanh) transformation is considered. Comparative performance analysis of the proposed IV method with the existing IV method, the data filtering-based LS methods, and the extended LS method shows improvement in the statistical properties of parameters estimates.

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Section: Introductionmentioning

confidence: 99%

Instrument variable method based on nonlinear transformed instruments for Hammerstein system identification

Saini

Dewan

2017

Journal of Vibration and Control

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“…The pitch angle controller has become most popular output power smoothening controller by maintaining the WECS output power at its rated value, during even a wide variation of wind speed. In recent days, controller application for WECS and smoothening of output power by pitch controller is achieved by various control techniques, such as, conventional PI control method (Wang et al., 2011), fuzzy logic control method (Chowdhury et al., 2012; Chen, 2014), hybrid control method (Duong et al., 2014), generalized predictive control method (Senjyu et al., 2006; Soundarrajan et al., 2012; Chen et al., 2014; Perng et al., 2014) and neural network control method (Yilmaz and Özer, 2009; Chen et al., 2013; Kuo et al., 2013). These control methods make sure that the generated power output of the WECS pursues the command value determined and offers steady output power.…”

Section: Introductionmentioning

confidence: 99%

An adaptive Elman neural network with C-PSO learning algorithm based pitch angle controller for DFIG based WECS

Sitharthan¹,

Geethanjali²

2016

Journal of Vibration and Control

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Frequent variation in the wind flow affects the Wind Turbine (WT) to generate fluctuating output power and this can negatively impact the entire power network. This paper aims at modelling an Enhanced-Elman Neural Network (EENN) based pitch angle controller to mitigate the output power fluctuation in a grid connected Wind Energy Conversion System. The outstanding aspect of the proposed controller is that, they can smoothen the output power fluctuation, when the wind speed is above or below rated speed of the WT. The proposed EENN pitch controller is trained online using Gradient Descent (GD) algorithm and the network learning is carried out using Customized-Particle swarm optimization (C-PSO) algorithm. The C-PSO is adopted, in order to increase the learning capability of the training process by adjusting the networks learning rate. Further, the node connecting weights of the EENN is updated by means of GD algorithm using back-propagation methodology. The performance of the proposed controller is analysed using the simulation studies carried out in MATLAB /Simulink environment.

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“…On other hand, much research effort has been put into the design of artificial neural network and fuzzy logic-based controllers as they reduce the complexity and allow a faster computation of the command [20][21][22][23][24][25][26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

A Fast Adaptive Artificial Neural Network Controller for Flexible Link Manipulators

Maouche¹,

Meddahi²

2016

ijacsa

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Nonlinear output feedback control of a flexible link using adaptive neural network: controller design

Cited by 8 publications

References 378 publications

Instrument variable method based on nonlinear transformed instruments for Hammerstein system identification

Instrument variable method based on nonlinear transformed instruments for Hammerstein system identification

An adaptive Elman neural network with C-PSO learning algorithm based pitch angle controller for DFIG based WECS

A Fast Adaptive Artificial Neural Network Controller for Flexible Link Manipulators

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