1. 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.Patel2
, J.Govindarajan3
, N.M.Nandhitha4
,
B.Sheela Rani5
1
Asst.Professor, Dept. of Electrical and Electronics Engg., Sathyabama University,
Jeppiaar Nagar, Old Mahabalipuram Road, Chennai 600 119
2
Engineer-SD, Electronics Group, Institute of Plasma Research.
3
Associate Professor-II,Institute of Plasma Research,
4
Professor & Head, Dept. of Electronics and Communication Engg., Jeppiaar Nagar,
Old Mahabalipuram Road, Chennai 600 119,
5
Vice 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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2. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME
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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

3. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME
326
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

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

5. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME
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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
[1] D. Wroblewski, G.L. Jahns and J.A. Leuer, ‘Tokamak disruption alarm based on a neural
network model of the high- beta limit ’, Nuclear Fusion, Vol. 37, Number 6, Issue 6 (June
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
SNR

6. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
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[3] A. Vannucci*
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