Performance evaluation of ann based plasma position controllers for aditya tokamak

International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –

6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME

324

PERFORMANCE EVALUATION OF ANN BASED PLASMA POSITION

CONTROLLERS FOR ADITYA TOKAMAK

J. Femila Roseline1, Jigneshkumar J.Patel

2, J.Govindarajan

3, N.M.Nandhitha

4,

B.Sheela Rani5

1Asst.Professor, Dept. of Electrical and Electronics Engg., Sathyabama University,

Jeppiaar Nagar, Old Mahabalipuram Road, Chennai 600 119 2Engineer-SD, Electronics Group, Institute of Plasma Research.

3Associate Professor-II,Institute of Plasma Research,

4Professor & Head, Dept. of Electronics and Communication Engg., Jeppiaar Nagar,

Old Mahabalipuram Road, Chennai 600 119, 5Vice Chancellor, Prof. Electronics & Instrumentation,

Sathyabama University, Chennai 600 119

ABSTRACT

In Aditya tokamaks, electrical energy is generated through plasma confinement in the

torroidal chamber. The amount of energy generated is directly related to the confinement of

the plasma within the chamber. Also if the plasma hits the limiters or the walls it leads to

plasma disruption. Extensive research has been done to develop controllers for confining the

plasma within the chamber. However these techniques had inherent limitations as they are

either linear models or fuzzy based controllers. The Fuzzy based controllers are strongly

dependent on the membership functions. Hence in this paper Artificial Neural Network based

classifiers are developed to overcome the limitations of the existing system. GRNN, RBN

based networks were developed and the performance is evaluated with that of the already

developed BPN based controller. It is found that BPN based controllers provide higher Signal

To noise ratio than the other controllers.

Keywords : Tokamaks, Plasma Position, plasma Confinement, radial position, plasma

current, BPN, voltage, RBN, GRNN;

INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING

& TECHNOLOGY (IJEET)

ISSN 0976 – 6545(Print) ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), pp. 324-329

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I. INTRODUCTION

In Aditya Tokamaks, hot plasma is contained by a magnetic field which keeps it away

from the machine walls. The combination of two sets of magnetic coils known as toroidal and

poloidal field coils creates a field in both vertical and horizontal directions, acting as a

magnetic toroidal chamber. The performance of the machine is dependent on the density of

the plasma, position of the plasma within the chamber and the time duration for which the

plasma is stabilized. From the literature, it is found that confinement of plasma within the

chamber yields better results in Aditya Tokamak. Plasma position control is basically a non-

linear process. However the initial controllers were linear PID controllers. Performance has

reduced as the constants can not be fixed accurately. Fuzzy based controller is the first non-

linear controller used for plasma position controller. However the performance of the system

is strongly dependent on the membership functions, defuzzification rules and the knowledge

base. Also certain assumptions made in Fuzzy based controllers are unrealistic in nature.

Hence it is necessary to develop an intelligent non-linear based controller that adapts to the

real time conditions and provides the results. General Recurrent Neural Network (GRNN)

and Radial Basis Function Network (RBFN) have been developed for controlling the plasma

current in Adithya Tokamak. The network accepts radial position and current as inputs and

predicts the stabilization voltage. The inputs and output variables for training and testing are

obtained from Aditya RZIP model.

The paper is organized as follows: Section II provides the related work. Section III gives an

overview of the neural networks chosen for developing plasma position controllers. The

proposed methodology is explained in section IV. Section V is about results and discussion

and Section VI concludes the work.

II. RELATED WORK

D. Wroblewski et al (1997) trained a neural network which combines signals from a

large number of plasma diagnostics and estimated the high- beta disruption boundary in the

DIII-D tokamak. The proposed neural network maps the disruption boundary throughout most

of the discharge. It can predict the high- beta disruption boundary on a time-scale of the order

of 100 ms (much longer than the precursor growth time), which makes this approach ideally

suitable for real time application in a disruption avoidance scheme [1]. J.V. Hernandez et al

(1996) described the use of neural network algorithms for predicting minor and major

disruptions in tokamaks by analyzing disruption data from the TEXT tokamak with two

network architectures. Fluctuating magnetic signal was extrapolated based on L past values of

the magnetic fluctuation signal measured by a single Mirnov coil [2]. A. Vannucci et al

(1999) used a neural network is trained with one disruptive plasma discharge and is validated

using soft X ray signals as input. After training they used the same set of weights to find out

the disruptions in two other plasma discharges and they observed that neural network is able to

predict the disruptions more than 3ms in advance when compared to the previously used

Mirnov coil [3]. Barbara Cannas et al proposed dynamic neural networks to predict the

plasma disruptions in a nuclear fusion device. Dynamic neural networks act as filters, which

predict one step ahead the value of diagnostic signals acquired during a plasma pulse [4]. A.

Sengupta et al (2002) developed two modified neural network techniques which are used for

indentifying equilibrium plasma parameters of the Superconducting Steady State Tokamak I

from external magnetic measurements. They used a multi network system which is connected

in parallel. By using this double neural network the accuracy of the recovered result is better



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than the conventional method. They fed the reduced and transformed input set rather than the

entire set, into the neural network input and called that as the principal component transformation-

based neural network [5]. A.B. Trunov (2004) developed several neural network approximators

which were computed on the basis of training data and analyzed their performance. It was found

that neural networks have better generalization properties than their linear counterparts, and can

therefore produce reasonably good prediction even with severely reduced input datasets [6].

III. OVERVIEW OF RBFN AND GRNN

Radial Basis Function Neural network (RBFN) consists of three layers: an input layer, a

hidden (kernel) layer, and an output layer. The nodes within each layer are fully connected to the

previous layer. The input variables are each assigned to the nodes in the input layer and they pass

directly to the hidden layer without weights. The transfer functions of the hidden nodes are RBF.

An RBF is symmetrical about a given mean or center point in a multidimensional space. A

Generalized Recursion Neural Network (GRNN) is a variation of the radial basis neural

networks, which is based on kernel regression networks. A GRNN does not require an iterative

training procedure as back propagation networks. It approximates any arbitrary function between

input and output vectors, drawing the function estimate directly from the training data. In

addition, it is consistent that as the training set size becomes large, the estimation error

approaches zero, with only mild restrictions on the function.

IV. RBFN AND GRNN BASED PLASMA POSITION CONTROLLERS

RBFN is chosen with two neurons in the input layer and one neuron in the output layer.

As the architecture of GRNN can not be modified the general four layered GRNN was chosen for

plasma position control. The exemplars are generated from Aditya RZIP model. Different sets of

exemplars are used for training and testing the neural network. A set of exemplars used for

training is shown in Table 1. The input parameters are the radial position and plasma current and

the output parameter is the plasma stabilization voltage. In order to prevent overflow the values

are normalized.

Table I.Exemplars Used For Trainiga The Neural Network

Input Parameters Output Voltage

Plasma current

(Ip) in A

Radial position

(Rp)

Desired output voltage (Va )

in Volts

60.0186 0.7498 0.0095

60.1467 0.7483 0.0759

60.4243 0.7443 0.2376

61.0378 0.7303 0.7360

61.5420 0.7003 1.6693

60.8311 0.6932 2.3222

60.2984 0.6891 3.3453

60.0457 0.6872 5.5227

60.0155 0.6873 4.5752

60.0188 0.6875 2.4668



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V. RESULTS AND DISCUSSION

The developed ANN is trained with 75 values. With the adapted weight, the ANN is

tested using another set of 70 values. The relationship between the actual and desired output for

the corresponding input parameters is shown in Table 1. From the last two columns of Table 1

the actual and desired values are nearly same. The relationship between actual and desired

values for different ANN is shown in figure 1. The performance of the ANN bases plasma

position controllers are tabulated in Table 2. Performance metrics are shown in Table 3. The

comparison of Signal to Noise Ratio for GRNN, RBN and BPN is shown in Figure 2.

Figer 1.Relation between desired and actual outputs for different ANN

TABLE II :PERFORMANCE OF ANN BASED PLASMA POSITION CONTROLLERS

Input Parameters Output Parameters

Plasma current

(Ip) in A

Radial

position

(Rp)

Desired output

voltage (Va) in

Volts

Actual output voltage (Va)

GRNN RBN BPN

0.9751 0.99999 0.0007 0.0010 0.0101 0.00075

0.9773 0.9977 0.0113 0.0113 -0.0086 0.0112

0.9918 0.9737 0.1092 0.0944 0.0995 0.4468

0.9752 0.9167 0.2279 0.4791 0.5007 -0.1627

0.9752 0.9168 0.4188 0.4695 0.4966 0.4209

0.9752 0.9187 0.4561 0.4558 0.4667 0.4562

0.9752 0.9194 0.4673 0.4672 0.4682 0.4673

0.9752 0.9207 0.4892 0.4892 0.4810 0.4893

0.9752 0.9213 0.5001 0.4999 0.4898 0.5003

0.9752 0.9220 0.5110 0.5092 0.4982 0.5111

0.9752 0.9226 0.5218 0.5151 0.5053 0.5053



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TABLE III.PERFOMANCE METRICS

Parameters GRNN RBN BPN

Root Mean

Square Error 0.0896 0.1002 0.0863

Standard

Deviation 0.0827 0.0854 0.0816

Signal to Noise

Ratio 4.8633 4.4095 5.2915

Figer 2.SNR comparison for GRNN, RBN and BPN

VI. CONCLUSION AND FUTURE WORK

GRNN and RBFN based plasma position controllers were developed successfully.

Exemplars were generated using Aditya RZIP model. The performance of these networks is

compared with that of BPN. Though GRNN and RBFN are best suited for predicting the

plasma stabilization voltage from incomplete set of exemplars, BPN based approach provides

better results in terms of Signal to Noise ratio and root mean square. As the exemplars data is

generated from Aditya RZIP model, the data is linear in nature. Hence it is necessary to test

and train the neural network with the plasma discharge shots obtained from Aditya Tokamak.

Also the feasibility of Neuro Fuzzy controller for plasma position control should also be

exploited.

REFERENCES

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1997)

[2] J.V. Hernandez, A. Vannucci, T. Tajima, Z. Lin, W. Horton and S.C. Mc Cool, ‘Neural

network prediction of some classes of tokamak disruptions ’, Nuclear Fusion, Vol. 36,

Number 8, Issue 8 (August 1996).

5.2915

4.8633

4.4095

0

1

2

3

4

5

6

GRNN RBN BPN

SN

R



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