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Volume 121, Issue 10,September 2010

www.sensorsportal.com ISSN 1726-5479

Editors-in-Chief: professor Sergey Y. Yurish, tel.: +34 696067716, fax: +34 93 4011989, e-mail:[email protected]

Editors for Western Europe

Meijer, Gerard C.M.,Delft University of Technology, The Netherlands

Ferrari, Vittorio, Universit di Brescia, Italy

Editor South America

Costa-Felix, Rodrigo, Inmetro, Brazil

Editor for Eastern EuropeSachenko, Anatoly, Ternopil State Economic University, Ukraine

Editors for North America

Datskos, Panos G., Oak Ridge National Laboratory, USA

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Editor for Asia

Ohyama, Shinji, Tokyo Institute of Technology, Japan

Editor for Asia-Pacific

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Enderle, Stefan, Univ.of Ulm and KTB Mechatronics GmbH, GermanyErdem, Gursan K. Arzum, Ege University, Turkey

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Sensors & Transducers Journal (ISSN 1726-5479) is a peer review international journal published monthly online by International Frequency Sensor Association (IFSA).Available in electronic and on CD. Copyright 2010 by International Frequency Sensor Association. All rights reserved.

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SSeennssoorrss && TTrraannssdduucceerrss JJoouurrnnaallCCoonntteennttss

Volume 121Issue 10October 2010

www.sensorsportal.com ISSN 1726-5479

Research Articles

Computational Sensor Network: Book ReviewSergey Y. Yurish................................................................................................................................. I

ANN Modeling of a Chemical Humidity Sensing MechanismSouhil Kouda, Zohir Dibi, Fayal Meddour, Abdelghani Dendouga and Samir Barra........................ 1

Design of Artificial Neural Network-Based pH EstimatorShebel A. Alsabbah, Maazouz A. Salahat and Mohammad K. Abuzalata......................................... 10Improved RBF Neural Network Based Soft Sensor: Application to the Optimal RobustCalibration of a Six Degrees of Freedom Parallel Kinematics ManipulatorDan Zhang and Zhen Gao.................................................................................................................. 18

Real Time Interfacing of a Transducer with a Non-Linear Process using SimulatedAnnealingS. M. GirirajKumar, K. Ramkumar, Bodla Rakesh, Sanjay Sarma O. V. and Deepak Jayaraj .......... 29

Visible and Near Infrared (VIS-NIR) Spectroscopy: Measurement and Prediction of SolubleSolid Content of AppleHerlina Abdul Rahim, Kim Seng Chia and Ruzairi Abdul Rahim. ............................................................................................................ 42

Control System Design for Cylindrical Tank Process Using Neural Model Predictive ControlTechniqueM. Sridevi, P. Madhavasarma, S. Sundaram..................................................................................... 50

Application of Genetic Algorithm for Tuning of a PID Controller for a Real Time IndustrialProcessS. M. Giri Rajkumar, Atal. A. Kumar, N. Anantharaman. ................................................................... 56

Modeling and Control of Multivariable Process Using Intelligent Techniques

Subathra Balasubramanian, Radhakrishnan T. K. ............................................................................. 68

Limitations of Feedback, Feedforward and IMC Controller for a First Order Non-LinearProcess with Dead TimeMaruthai Suresh and Ranganathan Rani Hemamalini....................................................................... 77

Embedded Based DC Motor Speed Control SystemChandrasekhar T., Nagabhushan Raju K., V. V. Ramana C. H., Nagabhushana KATTE and ManiKumar C.............................................................................................................................................. 94

Real Time Implementation of a DC Motor Speed Control by Fuzzy Logic Controller and PIController Using FPGAG. Sakthivel, T. S. Anandhi, S. P. Natarajan...................................................................................... 106

IDC Based Battery-free Wireless Pressure SensorJose G. Villalobos, Zhen Xu, and Yi Jia ............................................................................................. 121

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Energy Efficient MAC for Wireless Sensor NetworksPekka Koskela, Mikko Valta and Tapio Frantti................................................................................... 133

Authors are encouraged to submit article in MS Word (doc) and Acrobat (pdf) formats by e-mail: [email protected]

Please visit journals webpage with preparation instructions: http://www.sensorsportal.com/HTML/DIGEST/Submition.htm

International Frequency Sensor Association (IFSA).

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Sensors & Transducers Journal, Vol. 121, Issue 10, October 2010,pp. 56-67

56

SSSeeennnsssooorrrsss &&& TTTrrraaannnsssddduuuccceeerrrsssISSN 1726-5479

2010 by IFSA

http://www.sensorsportal.com

Application of Genetic Algorithm for Tuning of a PID

Controller for a Real Time Industrial Process

1S. M. Giri RAJKUMAR, 2Atal. A. KUMAR, 3N. Anantharaman1Department of Electronics and Instrumentation Engineering, School of Electrical and Electronics

Engineering, SASTRA University, Thanjavur-613402, India2SASTRA University, Thanjavur - 613 402, India,

3Department of Chemical Engineering, National Institute of Technology,

Tiruchirappalli - 620 015, India

E-mail: [email protected]

Received: 14 September 2010 /Accepted: 18 October 2010 /Published: 26 October 2010

Abstract: PID (Proportional Integral Derivative) controller has become inevitable in the process

control industries due to its simplicity and effectiveness, but the real challenge lies in tuning them to

meet the expectations. Although a host of methods already exist there is still a need for an advanced

system for tuning these controllers. Computational intelligence (CI) has caught the eye of the

researchers due to its simplicity, low computational cost and good performance, makes it a possible

choice for tuning of PID controllers, to increase their performance. This paper discusses in detail about

Genetic Algorithm (GA), a CI technique, and its implementation in PID tuning for a real time

industrial process which is closed loop in nature. Compared to other conventional PID tuning methods,

the result shows that better performance can be achieved with the proposed method.

Copyright 2010 IFSA.

Keywords: PID, CI, GA, REAL TIME SYSTEM.

1. Introduction

During the past decades, process control techniques in the industry have made great advances.

Numerous control methods such as: adaptive control, predictive control, neural control and fuzzy

control have been studied. Despite many efforts, the proportionalintegral derivative (PID) controller

continues to be the main component in industrial control systems, included in the following forms:embedded controllers, programmable logic controllers and distributed control systems. The reason is

that it has a simple structure which is easy to be understood by the engineers and it presents robust

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performance within a wide range of operating conditions. Van Overschee and De Moor [1] report that

80% of PID type controllers in the industry are poorly/less optimally tuned. They state that 30 % of the

PID loops operate in the manual mode and 25 % of PID loops actually operate under default factory

settings. Over the years, many techniques have been suggested for tuning of the PID parameters. In

this context there are classical (Ziegler/Nichols, gain phase margin method, Cohen/Coon and pole

placement) [2-5] and advanced techniques (minimum variance, gain scheduling and predictive)

[6- 10]. Some disadvantages of these control techniques for tuning PID controllers are: (i) excessivenumber of rules to set the gains, (ii) in adequate dynamics of closed loop responses, (iii) difficulty to

deal with nonlinear processes and (iv) mathematical complexity of the control design [10]. Therefore,

it is interesting for academic and industrial communities the aspect of tuning PID controllers,

especially with a reduced number of parameters to be selected and a good performance to be achieved

when dealing with complex processes [9].

During the last 25 years there has been significant developments in methods for model based control

[11, 12]. A recent survey of evolutionary algorithms for control systems can be found in [13, 14].

Among the techniques found out, intelligent techniques and computational optimization techniques

have found themselves a place in tuning of the parameters. The intelligent techniques include Artificial

Neural Networks (ANN), and Fuzzy Logic (FL) which have developed over the last ten years [15, 16].Neural and fuzzy logic mimic the functioning of human intelligence process [17]. Their real time

implementation is quite difficult [18], and hence as a result of the above said problems optimization

algorithms have received increasing attention by research community [19]. In recent years, there has

been extensive research on heuristic stochastic search techniques for optimization of the PID gains

[20, 21]. GA which is a part of evolutionary computation has shown to be a valuable and robust

technique in assisting engineers to solve complex problems [22-25]. Even a simple GA can give a

satisfactory result in a large variety of engineering optimization problem. Salhi gives a general

overview [26] of heuristic search methods including GAs.

Genetic Algorithm belonging to the family of evolutionary computational algorithms has been widely

used in many control-engineering applications. It is implemented as a computer simulation in which a

population of abstract representations, called chromosomes or the genotype or the genome, of

candidate solutions, creatures, or phenotypes, to an optimization problem evolves towards better

solutions [27]. It finds the optimal solution through cooperation and competition among the potential

solutions. These algorithms are highly relevant for industrial applications, because they are capable of

handling problems with non-linear constraints, multiple objectives, and dynamic components

properties that frequently appear in real-world problems [28]. It is an adaptive search algorithm

premised on the evolutionary ideas of natural selection. The basic concept of GA is designed to

simulate processes in natural system necessary for evolution, specifically those that follow the

principles first laid down by Charles Darwin of survival of the fittest. As such it represents an

intelligent exploitation of a random search within a defined search space to solve a problem. [29]. Thenatural process of evolution is mimicked in the algorithm to produce the best solution after many

cycles of cross-over, mutation and reproduction. The evolution usually starts from a population of

randomly generated individuals and happens in generations. In each generation, the fitness of every

individual in the population is evaluated, multiple individuals are stochastically selected from the

current population (based on their fitness), and modified (recombined and possibly randomly mutated)

to form a new population. The new population is then used in the next iteration of the algorithm.

Commonly, the algorithm terminates when either a maximum number of generations has been

produced, or a satisfactory fitness level has been reached for the population [30]. It is hence one of the

most developing controller tuning techniques [31]. The results obtained by the proposed method are

found better than the IMC techniques [32] in various aspects.

In the proposed work we compare the time domain specifications, the values of the performance

measures like integral of absolute error (IAE), the integral of time weighted square error (ITSE) and

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the integral of square time error (ISTE) [6, 33] obtained by using the conventional techniques and our

proposed method using genetic algorithm to prove that the proposed method is better than the

conventional methods. In the section that follows we have given the explanation of the setup, a view of

the conventional methods used, the proposed algorithm, the values obtained, the results and graphs and

finally the conclusion.

2. Industrial Process Based Closed Loop System

The closed loop system that has been considered here is used to maintain the temperature in an

agitated vessel. The agitator consists of three paddles which are connected to a vertical shaft. The shaft

is connected to an electric motor. The agitator is used to mix two acids namely sulphuric acid (H2SO4)

and oleum (H2S2O7). There are three inlets, for the inflow of sulphuric acid, oleum and steam. The

steam is used to supply heat to the agitating vessel and maintain it at a temperature of 110-1300C. The

steam is supplied from a 10 ton boiler through a regulator which supplies steam at a maximum

pressure of about 5 kgf/cm2. The mixed acid solution is then sent to the reactor through an overhead

pipeline. The steam from the boiler is sent through a pipeline. This line is connected as inlet to the

control valve which controls the steam entering the agitated vessel. A resistance temperature detector(RTD) is used to measure temperature in vessel. The range of RTD used is 0-200

0C. The output signal

is conditioned and converted to a current signal, which is of the standard range of 4-20 mA. Here,

4 mA corresponds to 00

C and 20 mA corresponds to 2000C of RTD. The output signal is given to the

host computer through the panel board of a Distributed Control System (DCS). Man-Machining

Interface allows human interruption in the process whenever necessary through the host computer,

which acts as a controller. Then the output signal from the computer is sent to a current to pressure

(I/P) converter. The signal is a current signal of 4-20 mA, the I/P converter is a device which gives out

pressurized air in the range of 3 15 psi to the control valve proportional to the current supply given to

it. The control valve is of globe type which is used to control the steam supplied to the agitated vessel.

The steam is supplied through metal pipes of about 2 diameter. When the pressure of the supply air to

the control valve from the I/P converter is 15 psi the valve will be 100 % opened and when the

pressure is 3 psi the valve will be 0 % opened. Thus the valve opening is proportional to the air

pressure supplied to it. The steam supplied to the control valve is about 4-5 kgf/cm2, which increases

the temperature suitably as per the requirement. The piping and instrument diagram of the process is

shown in Fig. 1.

Fig. 1. Piping and Instrument diagram of the industrial closed loop process.

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The industrial process system is further considered as a closed loop system with the components

having the specifications as indicated. Mathematical model for this process is estimated by considering

a step change of 10 % to the steam valve, after putting the system in an open loop mode. The response

curve was traced, and was found similar to be that of a FOPTD, and the mathematical model was

found to be,

)1328(468.0)(

42

sesG

s (1)

The model validation with its real time response is given in the Fig. 2.

Fig. 2. Comparison of real time and model response for the industrial process.

3. Non-Traditional Optimization Techniques

The implementation of non-traditional optimization techniques for a process based on temperature as

the variable to be controlled in a process industry has been attempted. A PID controller is proposed for

the system, which fulfills the need for anticipatory control. PID controllers are also considered more

suitable for temperature based processes. The transfer function of the process system based on the

operating conditions was estimated as in equation 1.

The conventional method chosen for the proposed work is called Internal Model Control (IMC)

technique. The various formulae used for this method for tuning the PID controller are given in

Table 1.

Table 1. Tuning rules for IMC technique.

Controller

Type

Controller Gain

(no units)

Integral Time

(seconds)

Derivative Time

(seconds)

PID

control

process dead time (seconds);process lag time (seconds);

K = process gain (dimensionless);

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used for aggressive but less robust tuning;

used for more robust tuning.

Some controller mechanisms use proportional band instead of gain. Proportional band is equal to

100 divided by gain. The values in the table are for an ideal type controller. The controller computes

controller gain, integral time, and derivative time using the formulas shown.

4. Genetic Algorithm

4.1. Introduction

Genetic Algorithm form a class of adaptive heuristics, based on principles derived from the dynamics

of natural genetics. The searching process simulates the natural evolution of biological creatures and

turns out to be an intelligent exploitation of a random search. A candidate solution (chromosome) is

represented by an appropriate sequence of numbers. In many applications the chromosome is simply a

binary string of 0s and 1s. The quality of its fitness function evaluates a chromosome, with respect tothe objective function of the optimization problem. A selected population of solution (chromosome)

initially evolves by employing mechanisms modeled similar to those used in Genetics. The Fig. 3

presents the flowchart for Genetic Algorithm.

Fig. 3. Flow chart for GA.

4.2. GA Operators

Reproduction

Reproduction is the first operator applied on a population. Reproduction selects goods strings in a

population and forms a mating pool and hence known as selection operator. There exist a number ofreproduction operators in GA, but the essential idea in all of them is to pick the above-average strings

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from the current population and insert their multiple copies in the mating pool in a probabilistic

manner.

In this work,Rank Ordermethod is used as reproduction operator. The rank is given according to the

fitness value of each chromosome. If fitness is more, higher the rank given, so that the probability (this

is fixed for less rank and more for high rank) for selecting that particular string is more.

The probability values for selection, according to

(Max-Min)(Rank (i, t)-1)

Expected value (i, t) = Min + ---------------------------------

N-1

Crossover

In crossover, new strings are created by exchanging information among strings of the mating pool

based on a probability Pc. Actually strings are picked from the mating pool and some portions of thestrings are exchanged between the strings. A string point crossover operator is used in this work which

is performed randomly by choosing a crossing site along the string and by exchanging all bits in on the

right side of the crossing site as shown.

Before crossover: The crossover site is selected as 7th

bit.

After crossover:

Crossover has been made between the strings after the 7th

bit.

Mutation

The mutation operator changes 1 to 0 and vice versa with a small mutation probability P m. Here the

operator performs a bit-wise mutation. The need for mutation is to create a point in the neighbor of the

current point, thereby achieving a local search around the current solution. Mutation is also used to

maintain diversity in the population.

Before mutation:

1101001000

After mutation:

1100001000 (here the mutation is carried out on the 4th bit)

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5. Implementation of GA

The optimal values of the conventional PID controller parameters Kp, Ki and Kd, is found using GA.

All possible sets of controller parameter values are chromosomes whose values are adjusted so as to

minimize the objective function, which in this case is the error criterion, which is discussed in detail.

For the PID controller design, it is ensured the controller settings estimated results in a stable closedloop system subjected to constraints.

5.1. Initialization of Parameters

To start up with GA, certain parameters need to be defined. It includes the population size, bit length

of chromosome, number of iterations, selection, crossover and mutation types etc. Selection of these

parameters decides to a great extent the ability of designed controller. Initializing the values of the

parameters for this work is as follows:

Population size 100

Bit length of the considered chromosome 6Number of Generations 100

Selection method Rank method

Crossover type Single point crossover

Crossover probability 0.8

Mutation type Uniform mutation

Mutation probability 0.05

5.2. Performance Index for the GA Algorithm

The objective function considered is based on the error criterion. The performance of a controller is

best evaluated in terms of error criterion. A number of such criteria are available and in this paper,

controllers performance is evaluated in terms of Integral of Absolute Errors (IAE) criterion, given by

equation 2.

(2)

The IAE weights the error with time and hence emphasizes the error values over arrange of 0 to T,where T is the expected settling time.

6. Results and Comparison

The implementation of GA is done to find the optimal PID controller parameters. They are plotted as

the best values among the considered population size for all the iterations, and are given in Figs. 4-6.

The PID controller parameters for this implementation is given as

Kp= 24.87, Ki=0.069 and Kd= 483.85.

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Fig. 4. Best solutions of Kp for 100 iterations for industrial process based on GA.

Fig. 5. Best solutions of Ki for 100 iterations for industrial process based on GA.

Fig.6. Best solutions of Kd for 100 iterations for industrial process based on GA.

The PID controller is designed for an industrial closed loop process for which the control variable is

temperature. The process is allowed to reach the steady state condition at 122 C , and the PID

controller is studied for its response by giving a servo change in the control variable by 2C, making

the new set point to be 124 C. The IMC controller is the best among the traditional techniques. Also,

the various PID controller parameters considered for analysis in this section are shown in the below

Table 2.

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The response of the controlled variable was sketched for the proposed PID controller, and is presented

in Fig. 7.

Table 2. Various PID controller parameters for industrial process.

Controllers IMC GA

Proportional gain, Kp 3.620 24.870Integral gain constant, Ki 0.0103 0.0690

Derivative gain constant, Kd 71.445 483.85

Fig. 7. Comparative response of IMC, GA based controllers for industrial process.

Based on these responses, the time domain specifications with relevance to the real time data, is noted

and they are tabulated and presented in the Table 3.

Table 3. Time domain specifications for industrial process system.

IMC GA

Inverse peak

(degree)119.8 121.36

Inverse peak

time(seconds)360 300

Rise time

(seconds)2500 660

Peak time

(seconds) 2500 1020

Overshoot

(%)1.2 86.5

Settling time

(seconds)2500 1920

The robustness investigation for the process is analyzed by calculating the performance index to the

transfer function model whose parameters are deviated by 20 %. The altered model which possesses

the uncertainties is given by,

)16.393(

561.0)(

6.33

s

esG

s

(3)

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The graph showing the variation of objective function as the iterations are carried on is shown below

(Fig. 8).

Fig.8. IAE values for 100 iterations for industrial process based on GA.

The calculation of the performance index for the mentioned model with the proposed controllers are

tabulated and presented in the Table 4.

Table 4. Performance index for industrial process model.

IMC GA

ITAE 999.03 53.97

IAE 948.11 162.55

ISE 952.63 343.75

MSE 0.0418 0.0151

The response curve with the IMC controller has a larger negative peak as the delay is not properly

taken care, whereas the GA controller is the best, proving to be a better one to achieve the set point.

Also, the robustness investigation illustrates the proposed tuning techniques always have a lesser value

than the traditional PID controller.

7. Results

The various results presented prove the betterness of the GA tuned PID settings than the IMC tuned

ones. The simulation responses for the models validated reflect the effectiveness of the GA based

controller in terms of time domain specifications. The performance index under the various error

criterions for the proposed controller is always less than the IMC tuned controller. Above all the real

time responses confirms the validity of the proposed GA based tuning for the industrial process

considered.

GA presents multiple advantages to a designer by operating with a reduced number of design methods

to establish the type of the controller, giving a possibility of configuring the dynamic behavior of the

control system with ease, starting the design with a reduced amount of information about the controller

(type and allowable range of the parameters), but keeping sight of the behavior of the control system.These features are illustrated in this work by considering the problem of designing a control system for

a plant of first-order system with time delay and deriving the possible results.

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