62-71 - Australian Journal of Basic and Applied Sciences

Australian Journal of Basic and Applied Sciences, 8(10) July 2014, Pages: 62-71
AENSI Journals
Australian Journal of Basic and Applied Sciences
ISSN:1991-8178
Journal home page: www.ajbasweb.com
Performance Testing of Non-Linear pH Neutralization Using Bacterial Foraging
Algorithm
1
M.Kandasamy and 2Dr.S.Vijayachitra
1
Department of Electronics and Instrumentation Engineering, Erode Sengunthar Engineering College, Erode -638057, India.
Department of Electronics and Instrumentation Engineering, Kongu Engineering College, Erode -638052, India
2
ARTICLE INFO
Article history:
Received 25 April 2014
Received in revised form
8 May 2014
Accepted 20 May 2014
Available online 17 June 2014
Keywords:
Bacterial Foraging Algorithm, pH
neutralization, performance indices,
first order plus time delayed (FOPTD)
system, Textile waste water Treatment.
ABSTRACT
Background: PID controllers are widely used in process industries to control linear,
non linear, stable and unstable systems. Selection of proper PID controller tuning
method and controller structure helps to improve the performance of the system. Textile
industry waste water process is highly non linear and complicated due to more number
of parameter variations. Objective: In this paper, Bacterial Foraging optimization
(BFO) Algorithm based PID controller design procedure is proposed for a pH
neutralization process widely employed in Textile waste water Treatment units.
Results: The efficiency of the proposed method is validated through a comparative
study with Ziegler-Nichols Method and Relay feedback Method. Also, the BFO
Algorithm is implemented on parallel PID controller, I-PD controller and set point filter
type PID controller in order to identify the best suitable controller structure for the pH
neutralization process. Conclusion: The BFO based set point filter type PID controller
shows an improved system performance compared with the parallel type PID controller
and I-PD controller considered in this study. The BFO based set point filter type PID
controller offers better set point tracking, load disturbance rejection, improved time
domain specifications, and minimal error compared to the alternatives considered in
this paper.
© 2014 AENSI Publisher All rights reserved.
To Cite This Article: M.Kandasamy, Dr.S.Vijayachitra., Performance Testing of Non-Linear pH Neutralization Using Bacterial Foraging
Algorithm. Aust. J. Basic & Appl. Sci., 8(10): 62-71, 2014
INTRODUCTION
Textile industry process is more complicated due to the presence of variety of raw materials, products,
processes and instruments. After the array of sub-processes like bleaching, dyeing, printing and stiffening, the
raw material (fibre) will reach the desired finishing product. In this, many of the sub-processes are wet
processing and the use of huge volume of water has become the reason for discharging high volume of effluent.
This effluent possesses broadly fluctuating pH, high range of Bio Oxygen Demand (BOD), Chemical Oxygen
Demand (COD), Suspended Solids (SS) and rich in Color. The presence of toxic and non-biodegradable organic
pollutants in the waste water is the biggest challenge for sustaining the environmental safety. Also due to water
scarcity, the high volume of waste water is treated effectively for re-usage to reduce the operational cost of the
textile industries.
Most pollutants in textile wastewater are weak organics acid or base, so the pH value of wastewater can
influence the property of pollutants. The pH in the range of 4 to 11 for treatment of textile industrial wastewater
is neutralized by using either H2SO4 or NaOH to adjust the pH of the solution (Nordin et al., 2013). Studies on
pH-neutralization dynamics and control are started in the year 1970 (McAvoy et al., 1972; Buchholt et al.,
1979). Since the process is highly nonlinear characteristics and uncertainty, research is still reported by many
scholars but using recent emerging control strategies (Chen et al., 2011). The waste water treatment is existed in
many chemical process industries and perfect practical control is yet been identified including for pH
neutralization. In spite of some successful practical applications, there still is no all-inclusive procedure or
method to design such intelligent controllers by far because of its semi-empirical nature. (Wan et al., 2010)
More research is carried out for modeling the Non Linear system by variety of methods including firstprinciple approach (Patwardhan and Madhavan, 1998). Many efforts have been attempted to design a nonlinear
black-box model with a merit of simple procedure, however selection of an exact model structure to capture
nonlinear dynamics over a wide operating range is difficult in the nonlinear black-box model.
Proportional, Integral, Derivative (PID) controllers are widely used in process control applications for the
kind of stable, unstable and nonlinear processes due its simplicity and performance characteristics
Corresponding Author: M. Kandasamy, Department of Electronics and Instrumentation Engineering, Erode Sengunthar
Engineering College, Erode -638057, Tamil Nadu,
E-mail: [email protected]
63
M. Kandasamy and Dr. S. Vijayachitra, 2014
Australian Journal of Basic and Applied Sciences, 8(10) July 2014, Pages: 62-71
(Rajinikanth and Latha, 2011; 2012; 2012a). The I term ensures robust steady-state tracking of step input and P
& D terms provide stability and desirable transient behavior of the system.
In control literature, many efforts have been attempted to design optimal controllers for non linear process
control systems. Nuella et al., (2009) have proposed a method of an adaptive Proportional+Integral +Derivative
(PID) controller design for nonlinear process control and in this, the PID parameters are obtained from
controller database according to the current process dynamics characterized by the information vector at each
sampling instance. Kim and Oh (2000) have suggested a nonlinear fuzzy PID control method, which can stably
improve the transient responses of systems disturbed by nonlinearities or unknown mathematical characteristics.
Also, Christoph Hametnera et al., 2013 have proposed discrete-time local model networks for PID controller
design for nonlinear systems.
Most preferred and widely accepted PID Controller tuning methods such as Ziegler -Nichols (Z-N) Method
(Ziegler and Nichols, 1942), Relay feedback Method (Astrom and Hagglund, 1984) are extensively addressed
by the researchers to solve complex process control engineering problems. Passino (2002) has proposed the
BFO algorithm based adaptive controller for a liquid level control problem. The perception of foraging activities
of Escherichia coli (E. coli) bacteria is used for the optimization technique to find out the best fitted PID
controller parameters by a set of artificial bacteria in the “D” dimensional search space. Many attempts by
researchers have been carried out to find the optimal controller parameters using Bacterial Foraging Algorithm
for different categories of engineering optimization problems ( Biswas et al., 2010;Chen et al., 2011).
In the proposed work, the performance of pH neutralization process is tested by using PID controller and
the PID parameters are tuned by various methods such as Ziegler -Nichols (Z-N) Method, Relay feedback
Method and Bacterial Foraging Algorithm (BFO) method. This work indicated that performance of the BFO
algorithm based PID controller is better than Z-N method and relay feedback method. Later, the BFO algorithm
is implemented on parallel PID controller, I-PD controller and Set point filter PID controller to find out best
controller structure for the pH neutralization process.
Finally a comparative study on optimal performance indices such as Integral of Squared Error (ISE),
Integral of Absolute Error (IAE), Integral of Time-weighted Squared Error (ITSE), Integral of Time weighted
Absolute Error (ITAE), Rise Time (tr), Peak Time (tp), Percentage of Peak Overshoot (Mp) have been carried
out.
The paper is organized as follows: Section 2 provides the design structure of parallel PID, I-PD and Set
point Filter PID controllers. The section 3 presents the PID controller tuning by Z-N method, Relay feedback
method and BFO algorithm method. Section 4 presents an overview of BFO algorithm. The simulated results of
process model of the pH neutralization process is discussed in the section 5 followed by the conclusion of the
research work in Section 6.
PID Controller Structures:
In process industries, PID controllers are implemented to obtain better steady state and transient response as
well as to maintain stability, smooth reference tracking and load disturbance rejection of the process. In a closed
loop control system, the controller continuously adjusts the final control element until the difference between
reference input and the process output is zero irrespective of the internal and/or external disturbance signal
(Panda, 2009).
Mathematical Model of an ideal PID controller is presented below:
Gc(s) = k 1  1   s 
p
d 
 is

and it can be modified as
K


 K p  i  Kd s 
s


Where, Integral Time   K p and Derivative Time   K d
i
d
Ki
Kp
(i) Parallel PID Controller Tuning:
Fig. 1: Block diagram of closed loop control systems.
(1)
(2)
64
M. Kandasamy and Dr. S. Vijayachitra, 2014
Australian Journal of Basic and Applied Sciences, 8(10) July 2014, Pages: 62-71
Fig. 2: Block diagram of Parallel PID control systems.
The Fig. 1 shows the basic block diagram of closed loop control systems. In this, G c(s) is the PID controller
and Gp(s) is the process to be controlled. Also the R(s) is reference signal; Y(s) is controlled (output) signal;
E(s) is error signal; Uc(s) is controller output; A simple PID controller is act as controller „G c(s)‟ to control the
process.
The Fig. 2 shows the structure of basic parallel PID controller. The following mathematical models of
parallel PID controller structure described in the equations (3) and (4) are used in most of the process and
considered for this study.
K


G C (s)   K p  i  K d s 
s


(3)
The controller output is given as
T
U C (t)  K p e(t)  K i  e(t) dt  K d
0
de(t)
dt
(4)
(ii) I-PD Controller Tuning:
I-PD controller is customized version of PID Controller. The demerit of the parallel PID structure is, when
a step signal is given as reference signal „R(s)‟ to the controller, it will produce immediate spike as output. This
abrupt change in the controller behavior is called as proportional and Derivative kick. These unwanted kick
effects suddenly cause the entire process „Gp(s)‟ into more critical. This drawback is eliminated by the
implementation of I-PD controller which is shown in Fig. 3.
In this structure, the integral term alone acting on the error signal „E(s)‟ and the abrupt change in the
reference signal „R(s)‟ will not affect the proportional and derivative terms since these two terms are acting on
output of the system „Y(s)‟. This I-PD controller is widely preferred in the industries to get smooth set point
tracking due to the absence of the „kick effect‟. The output of the I-PD controller is given in the equation (5)
U C (t)  K i 
T
0
d y(t) 

e (t) dt - K p y(t)  K d
dt 

(5)
Fig. 3: Block diagram of I-PD control systems.
(iii) Set point Filter PID Controller Tuning:
Peak Over shoot is one of the most important specifications in all the process plants. The system that
produces high overshoot is not preferred because; large overshoot brings the process into worst scenario. Set
point filter PID controllers are widely used in the industries to limit the value of overshoot (Vijayan and Panda,
2012; 2012a). A set point filter is added with the parallel PID to obtain the filter PID controller.
65
M. Kandasamy and Dr. S. Vijayachitra, 2014
Australian Journal of Basic and Applied Sciences, 8(10) July 2014, Pages: 62-71
Fig.4: Block diagram of filter PID control systems.
The Fig. 4 shows the basic structure of the set point filter PID and the equation (6) provides the
mathematical model,

1
 d s  1 
(6)
K p 1 



 is  d s  1  f s  1

Where, α is derivative filter Constant and it is assigned as 10 in this study.  f is set point filter parameter and its
value obtained as
 f = Kp/Ki (Jung et al., 1999). Where KP and Ki are proportional gain and Integral gain of
the controller respectively .
Tuning of PID Controllers:
The following controller tuning process is used to find the best optimum values of K p, Ki, Kd to confirm the
minimum time domain specifications and error values in this study.
(i) Ziegler and Nichols Method:
In 1942, Ziegler and Nichols have proposed simple mathematical procedures for tuning PID controllers.
These procedures are widely accepted and treated as standard in control systems practice. In this method,
Integral gain Ki and derivative gain Kd are set with zero and proportional gain Kp is increased to specific critical
value to make sustained oscillation output. From the procedure, the optimum controller parameters for the PID
controller is obtained
(ii) Relay feedback Method:
Astrom and Hagglund (1984) suggested the relay feedback test to generate sustained oscillations as an
alternative to the conventional continuous cycling technique. Since it is the closed loop test, the process will not
drift away from the nominal points as well as it identifies process information around the important frequency to
obtain the controller parameters.
(iii) Bacterial Foraging optimization (BFO) Method:
Initially, the boundary values of PID is to be assigned to guide the optimization algorithm and to attain the
good accuracy. Many researchers have proposed the Multiple Objective Performance Index (MOPI) such as
overshoot (Mp), settling time (ts), steady state error (ess), rise time (tr), gain margin (GM) and phase margin (PM)
for PID controller optimization (Rajinikanth and Latha, 2012b; Zamani et al., 2009). The following equation
describes the parameters selected for MOPI to find the controller Parameter K p, Ki and Kd by BFO algorithm.
Jmin(Kp,Ki,Kd) = (w1 · ISE) + (w2 · IAE) + (w3 ·Mp) + (w4 · ts) + (w5 · tr )
(7)
Where Jmin (Kp,Ki,Kd) - Performance criterion
ISE - Integral Square Error
IAE - Integral absolute Error
Mp = Peak Overshoot is the difference between maximum peak value of the response curve c(tp) and final value
of c(t)
ts = Settling time is time required for the response curve to reach and stay within 2% of the final value.
tr = Rise time is time required for the response to rise from 0% to 100% of its final value.
w1, w2, w3, w4 and w5 are weighting functions of the MOPI parameters and the value of “w” varies from 0 to 10.
Fig. 5: BFO algorithm-based PID controllers tuning.
66
M. Kandasamy and Dr. S. Vijayachitra, 2014
Australian Journal of Basic and Applied Sciences, 8(10) July 2014, Pages: 62-71
The Fig. 5 shows the basic structure of BFO algorithm based PID controller tuning controllers tuning. The
similar modified structure can be obtained to incorporate BFO based I-PD controllers tuning and set point Filter
PID.
The following parameters are assigned to BFO and MOPI as the preliminary process for optimization
search. Dimension of the search is assigned as three ( K p, Ki, Kd); number of E. coli bacteria as is ten; number
of reproduction steps is assigned as four; length of a swim considered as four ; number of chemo tactic steps is
selected as five; number of elimination-dispersal events are considered as two; number of bacterial reproduction
is set as five, probability for bacteria eliminated /dispersed is considered as „0.25‟; d att is assigned as zero ; Watt
is set as „0.5‟ hrep is considered as „0.6‟ and Wrep is assigned as „0.6‟.
• The limits of the three dimensional search space is as
 Kp = 0% < Kp < +50%
 Ki = 0% < Ki < +25%
 Kd = 0% < kd < +50%
• The weighting function values are assigned as w1 =w2 = w3 = 10, w4 = w5 = 6.
• The reference input signal „R(s)‟ is unity.
• The “tr” is chosen as <25% of the maximum simulation time. The settling time „t s‟ is selected as <50% of
the maximum simulation time.
• The overshoot in the process output „Mp‟ is considered as <10% of the reference signal.
• The steady state error (ess) of process output is assigned as zero.
• Maximum simulation time is 100 sec. The simulation time is selected based on the process time delay.
• Ten trials are carried out for each algorithm and among them best value is considered as suitable optimized
controller value.
Bacterial Foraging Optimization Algorithm:
Bacterial Foraging Optimization (BFO) algorithm is a new division of biologically inspired computing
technique introduced by Passino in 2000. It is based on mimicking the foraging methods for positioning,
handling and ingesting food behaviour of Escherichia coli (E. coli) bacteria living in human intestine. The
algorithm has an advantage of high computational efficiency, simple design procedure, and stable convergence.
The flow chart shown in Fig. 6 is brief about the BFO algorithm and its basic operations with key process.
Fig. 6: Flow chart for bacterial foraging algorithm.
Chemo-taxis:
This process simulates the movement of an E.coli cell towards the food source with swimming and
tumbling action via flagella. The bacteria can move in a particular path by swimming and can modify the
67
M. Kandasamy and Dr. S. Vijayachitra, 2014
Australian Journal of Basic and Applied Sciences, 8(10) July 2014, Pages: 62-71
direction of search during tumbling action. These two modes of operations are endlessly executed by a bacteria
its whole lifetime to reach the sufficient amount of positive nutrient gradient.
Swarming:
This process is carried out by the bacteria to acknowledge the information about optimum path of the food
source with other bacteria. An attraction signal is produced for this communication between the cells in the Ecoli bacteria. Another repellent signal is also produced for noxious reserve. This process helps them to increase
the bacterial density at the identified food position in the chemotaxis. The attraction signal is represented by the
below equation (8).
s
J cc ( (i, j , k , l ))   J cc ( , i ( j , k , l ))  X  Y
i 1
Where
s 
n


X    d att exp  watt  ( m   mi ) 2  and
i 1 
m1



(8)
s 
n


Y    hrep exp  wrep  ( m   mi ) 2 
i 1 
m 1



Where “s” = Total number of bacterium, “n”= Total parameters to be optimized, datt = Depth of attractant
signal released by a bacteria, “Watt” = Width of attractant signal, “hrep” = height of repellent signals between
bacterium, “Wrep” = weight of repellent signals between bacterium and J cc(θ,(i,j,k,l)) is the objective function
value. “θ” is the point in the n dimensional search domain till the jth chemotactic, kth reproduction and lth
elimination. Also “θm” is the mth parameter of global optimum bacteria
Reproduction:
In swarming process, the bacteria gathered as groups in the positive nutrient gradient and which may
increase the bacterial density. Later, the bacteria are arranged in descending order based on its health values.
The least healthy bacteria eventually expire while healthier bacteria asexually split into two bacteria and
maintain the predefined population.
Elimination-Dispersal:
This is the closing phase in the bacterial search. The bacterium population may decrease either gradually or
suddenly depend on the environmental criteria such as change in temperature, and availability of food etc.
Significant local rise of temperature may kill a group of bacteria that are currently in a region with a high
concentration of nutrient gradients. Actions may take place in such a way that all the bacteria in a location are
killed and eliminated (local optima) or a group is relocated (dispersed) into a new food source. The dispersal
possibly compresses the chemo-taxis advancement. After dispersal, some bacteria may be located near the
superior nutrient and this process is called “Migration”. The above events are continued until the entire
dimensional search converges to optimal solutions or total number of iterations is reached
RESULTS AND DISCUSSIONS
The closed loop performance of the pH neutralization is analysed with BFO based controllers by using
mathematical model of the process from literature (Meenakshipriya et al., 2012).
The pH neutralization process considered for the proposed analysis is shown in Fig. 7. In this, pH is
controlled by controlling the flow rate of acid due to alkalinity nature of textile waste water. The mathematical
model obtained for the process is given in the equation (9)
G (s) 
7.0921 e 1.71s
8.54 s  1
Fig. 7: pH neutralization system.
(9)
68
M. Kandasamy and Dr. S. Vijayachitra, 2014
Australian Journal of Basic and Applied Sciences, 8(10) July 2014, Pages: 62-71
For analysing the performance of pH neutralization process, the PID controller parameters (Kp, Ki, Kd) are
obtained by Z-N method, Relay Feedback method and BFO algorithm and their performance are taken into
consideration. The table 1 shows the performance indices of Z-N method, Relay Feedback method and BFO
algorithm method.
Table1: Performance indices of different PID controller tuning.
% of Mp
tr (sec.)
ts (sec.)
Z-N
93.2
4.2
45.0
Relay
81.0
4.5
35.0
feedback
BFO
34.0
5.05
12.0
Algorithm
IAE
10.57
8.03
ISE
6.02
4.67
ITAE
112.30
63.20
ITSE
35.65
20.44
4.397
2.91
15.56
5.38
2
1.8
Reference Tracking
1.6
1.4
1.2
1
0.8
0.6
Set Point
0.4
BFO-PID
ZN-PID
Relay-PID
0.2
0
0
10
20
30
40
50
60
Time (sec)
Fig. 8: Comparison of Servo Response of different PID Controllers Tuning.
1
Controller Output
0.5
0
BFO-PID
-0.5
0
10
20
30
Time (Sec)
ZN-PID
40
Relay-PID
50
60
Fig. 9: Comparison of Controller response for different PID Controllers Tuning.
2.5
Reference Tracking
2
1.5
1
0.5
Set Point
0
-0.5
0
20
40
60
80
BFO-PID
100
ZN-PID
120
Relay-PID
140
160
Time (sec)
Fig. 10: Regulatory Response for different PID Controllers Tuning.
1
0.8
Controller Output
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
BFO-PID
ZN-PID
Relay-PID
-0.8
-1
0
20
40
60
80
100
120
140
160
Time (sec)
Fig. 11: Controller output for Regulatory Response of different PID Controllers Tuning
The performance of a controller can be tested by mode of servo control and regulatory control. In the servo
control mode, the objective of controller is to provide accurate tracking of reference signal. The Fig. 8 and Fig. 9
show the servo response of the different PID controllers and corresponding controller output respectively. The
69
M. Kandasamy and Dr. S. Vijayachitra, 2014
Australian Journal of Basic and Applied Sciences, 8(10) July 2014, Pages: 62-71
set point tracking (Servo Response) is the most important requirement for a controller. The controller having
faster set point tracking is always preferred in the process industries. Simultaneous Set point tracking is carried
out for the considered three controllers and its result is shown in the Fig. 8. It is clearly observed that BFO based
PID controller provides better set point tracking compared with the ZN method and Relay feedback method.
Frequently varying pH values due to different parameter variation is a major problem in the waste water
treatment process. The fitness of the controller can be judged by testing the performance of the controller under
the load change condition. The performance of the controller is studied by applying load disturbance to the
process model, which is shown in the Fig. 10 and Fig. 11. Simultaneously all the three controllers are applied
with a disturbance and it can be noted that the BFO algorithm based controller eliminates the effect of
disturbance much faster than Z-N method and Relay feedback method. The BFO based controller exhibits an
IAE value of 4.397 against 8.03 for relay feedback method. The IAE value of Z-N method is 10.57. Also the
table 1indicates that BFO Algorithm based PID controller has overall better performance Indices than the other
two controller tuning methods.
Table 2: Performance indices of different controller structures.
Structure
% of Mp
tr (sec)
ts (sec)
PID
34.0
5.05
12.0
I-PD
5.42
15.0
25.0
FPID
00.0
22.0
24.0
IAE
4.39
7.72
6.76
ISE
2.91
5.08
4.93
1.4
1.2
Reference tracking
1
0.8
0.6
0.4
Setpoint
PID
I-PD
FPID
0.2
0
0
10
20
30
40
50
Time (sec)
Fig. 12: Comparison of Servo Response of different PID structures.
0.6
Controller output
0.5
0.4
0.3
PID
I-PD
FPID
0.2
0.1
0
-0.1
0
10
20
30
40
50
Time (sec)
Fig. 13: Comparison of Controller response for different PID structures.
1.4
1.2
Response
1
0.8
0.6
0.4
Setpoint
0.2
0
0
20
PID
I-PD
40
60
FPID
80
100
Time (sec)
Fig. 14: Regulatory Response for different structures.
0.6
Controller output
0.5
0.4
0.3
0.2
0.1
0
-0.1
0
PID
20
I-PD
FPID
40
60
Time (sec)
Fig. 15: Controller output for Regulatory Response.
80
100
ITAE
15.56
41.72
30.01
ITSE
5.38
17.93
13.92
70
M. Kandasamy and Dr. S. Vijayachitra, 2014
Australian Journal of Basic and Applied Sciences, 8(10) July 2014, Pages: 62-71
In continuation of the first study, second analysis is carried out to evaluate the best PID controller structure
for the pH Neutralization process. The parallel PID controller, I-PD controller and Set point filter PID
controller are considered for this analysis. Multiple Objective Performance Index (MOPI) based BFO algorithm
is used to find out the optimum controller parameters to study the performance of different PID controller
structures.
A simulation study is carried out to evaluate the set point tracking (servo response) performance of the three
different controllers simultaneously. The Fig. 12 and Fig. 13 show the servo response of the different PID
structures and corresponding controller output respectively. The Table 2 shows the performance indices of
different PID structures. It is noted that the parallel PID controller may not be preferred for practical
implementations due to its large peak overshoot value. It is interpreted that the I-PD controller has better peak
overshoot compared to the parallel PID controller. However, the most importantly, the filter PID has zero
overshoot.
Load disturbance is applied to the process model to study the regulatory response of different PID controller
tuning. The Fig. 14 and Fig. 15 show regulatory response and corresponding controller output of the controllers.
It can be observed that filter PID controller eliminates the effect of disturbance much faster than other two
controllers. The filter PID controller provides the minimum value of IAE such as 6.76 against 7.72 of I-PD
controller. Also, the filter PID controller show Better performance in error minimizing indices such as ISE,
ITAE, ITSE and settling time. In overall, the Set point filter PID controller has performed well compared with
parallel PID and I-PD controller for the pH Neutralization plant.
Conclusion:
A Bacterial Foraging optimization (BFO) Algorithm based PID controller tuning is proposed for process of
Non-Linear pH Neutralization. The proposed method produced better result compared to Ziegler-Nichols and
Relay feedback controller tuning methods in obtaining performance indices of IAE, ISE, ITAE, ITSE and closed
loop performance of peak overshoot, rise time and settling time. The BFO algorithm PID controller result is
again tested on two different controller structures i.e, I-PD Controller and Filter PID controller for further study.
In this, Filter PID controller exhibits better closed loop performance and better performance indices. The
controllers are also tested for set point tracking and disturbance rejection. In this, the BFO based Filter PID
controller has efficiently tracks set point and provides desired load disturbance properties compared with
parallel PID and I-PD controller.
REFERENCES
Astrom, K.J. and T. Hagglund, 1984. Automatic tuning of simple regulators with specifications on phase
and gain margins. Automatica,. 20(5): 645-651.
Biswas, A., S. Das, A. Abraham, and S. Dasgupta, 2010. Stability analysis of the reproduction operator in
bacterial foraging optimization. Theoretical Computer Science, 411(2): 2127-2139.
Buchholt, F. and M. Kummel, 1979. Self-tuning Control of pH-Neutralization Process, Automatica, 15:
665-671.
Chen, H., Y. Zhu, and Hu, K. 2011. Adaptive bacterial foraging optimization. Abstract and Applied
Analysis, vol. 2011, Article ID 108269, 27.
Christoph Hametner, Christian H. Mayr, Martin Kozek and Stefan Jakubek, 2013. PID controller design for
nonlinear systems represented by discrete-time local model networks. International Journal of Control, 86:
1453-1466.
ImmaNuella, Cheng Cheng and Min-Sen Chiu, 2009. Adaptive PID Controller Design for Nonlinear
Systems. Industrial Engineering Chemistry Research, 48: 4877-488.
Jung, C.S, H.K. Song and J.C. Hyun, 1999. A direct synthesis tuning method of unstable first-order-plustime-delay processes. J. Process Control, 9: 265-269.
Kim, J.H. and S.J. Oh, 2000. A fuzzy PID controller for nonlinear and uncertain systems. Soft Computing Springer Link, 4: 123-129.
Meenakshipriya, B., K. Saravanan, K. Krishnamurthy and P.K. Bhaba, 2012. Design and implementation of
CDM-PI control strategy in pH neutralization system. Asian Journal of Scientific Research, 5(3): 72-92.
McAvoy, T.J., E. Hsu, and S. Lowenthal, 1972. Dynamics of pH in Controlled Stirred Tank Reactor.
Ind.Engrg. Chem. Process Des. Develop., 11: 68-70.
Norazzizi Nordin, SitiFathritaMohd Amir, Riyanto, and Mohamed Rozali Othman, 2013. Textile Industries
Wastewater Treatment by Electrochemical Oxidation Technique Using Metal Plate. International Journal of
Electrochemical Science, Vol. 8:11403 – 11415.
Panda, R.C., 2009. Synthesis of PID controller for unstable and integrating process. Chem. Eng. Sci., 6:
2807-2816.
Passino, K.M., 2002. Biomimicry of bacterial foraging for distributed optimization and control. IEEE
Control Systems Magazine, 22(3): 52–67.
71
M. Kandasamy and Dr. S. Vijayachitra, 2014
Australian Journal of Basic and Applied Sciences, 8(10) July 2014, Pages: 62-71
Patwardhan, S.C. and K.P. Madhavan, 1998. Nonlinear internal model control using quadratic prediction
models. Computers and Chemical Engineering, 22: 587-601.
Rajinikanth, V. and K. Latha, 2011. Bacterial foraging optimization algorithm based PID controller tuning
for time delayed unstable system. The Mediterranean Journal of Measurement and Control, 7(1): 197-203.
Rajinikanth, V. and K. Latha, 2012. I-PD Controller Tuning for Unstable System Using Bacterial Foraging
Algorithm: A Study Based on Various Error Criterion, Applied Computational Intelligence and Soft Computing,
Volume 2012, Article ID 329389, 10.
Rajinikanth, V. and K. Latha, 2012a. Controller Parameter Optimization for Nonlinear Systems Using
Enhanced Bacteria Foraging Algorithm, Applied Computational Intelligence and Soft Computing, Volume
2012, Article ID 214264, 12.
Rajinikanth, V. and K. Latha, 2012b. Setpoint weighted PID controller tuning for unstable system using
heuristic algorithm, Archives of Control Science, 22(4): 481-505.
Vijayan, V. and Rames C. Panda, 2012. Design of PID controllers in double feedback loops for SISO
systems with set-point filters. ISA Transactions, 5: 514-521.
Vijayan, V. and Rames C. Panda, 2012a. Design of setpoint filter for minimizing overshoot for low order
process. ISA Transactions, 51: 271-276.
Wan, J.Q., M.Z.Y.W. Huang, W.J. Ma and Guo, Y. Wang, and H.P. Zhang, 2010. Control of the
Coagulation Process in a Paper-mill Wastewater Treatment Process Using a Fuzzy Neural Network. Journal of
Bio chem., 425-435.
Xiaohui Chen, Jinpeng Chen and Bangjun Lei, 2011. Identification of pH Neutralization Process Based on
the T-S Fuzzy Model. Advances in Computer Science, Environment, Eco informatics, and Education
Communications in Computer and Information Science, 215: 579-58.
Zamani, M., N. Sadati and M.K. Ghartemani, 2009. Design of an H∞, PID controller using particle swarm
optimization. International Journal of Control, Automation and Systems, 7(2): 273–280.
Ziegler, J.G. and N.B. Nichols, 1942. Optimum settings for automatic controllers. Trans. ASME, 64759768.

Download Report