Control theory

Control Theory and Informatics www.iiste.org
ISSN 2224-5774 (print) ISSN 2225-0492 (online)
Vol 2, No.4, 2012

Design of PID Controller for Higher Order Discrete Systems Based
on Order Reduction Employing ABC Algorithm

G.Vasu1* G.Sandeep2

1. Assistant professor, Dept. of Electrical Engg., S.V.P Engg College, Visakhapatnam, A.P, India

2. B.E, Dept. of Electrical Engineering, S.V.P Engineering College, Visakhapatnam, A.P, India.

* E-mail of the corresponding author: vasuganji_kvs@yahoo.com
ABSTRACT

This paper proposes a new computational simple scheme for Model Order Reduction to design a discrete
PID controller for higher order linear time invariant discrete systems. Artificial Bee Colony (ABC)
optimization algorithm is employed for both order reduction and controller design. First a successful
reduced order model is obtained for original higher order discrete system using ABC optimization
algorithm which is based on the minimization of integral square error between the original and reduced
order models pertaining to step input. Then a PID controller is designed for reduced order model, based on
the minimization of integral square error between the desired response and actual response, pertaining to a
unit step input using ABC algorithm. Finally the designed PID controller is connected to the original higher
order discrete system to get the desired specifications. The validity of the proposed method is illustrated
through a numerical example.

Keywords: Discrete system, Model order reduction, PID controller, Integral square error, Artificial Bee
Colony algorithm.

I. INTRODUCTION
Scientists and Engineers are confronted with the analysis, design and synthesis of real life problems. The
first step in such studies is the development of a ‘Mathematical Model’ which can be utilized for the real
problem. The mathematical procedure of system modeling often leads to comprehensive description of a
process in the form of higher order differential equations. These equations in frequency domain leads to a
higher order transfer function which is difficult to use either for Analysis or controller synthesis. Therefore,
it is necessary to find lower order transfer function which maintains dominant characteristics of the original
higher order. Reduction of higher order systems to low order models has also been an important subject
area in control engineering for many years. There are two approaches for the reduction of discrete systems,
namely the indirect method and direct method. The indirect method [1-3] uses some transformation and
then reduction is carried out in the transformed domain. First the Z-domain transfer function is converted
into S-domains by linear or bilinear transformation and then after reducing in S-domain suitably, they are
converted back into Z-domain. In the direct method the higher order Z-domain transfer function is reduced
to lower order transfer function in same domain without any transformation [4-5].

Design of controller based on reduced order model is called process reduction technique. During the past
decades, the process control techniques in the industry have made great advances. The process approach is
computationally simpler as it deals with reduced order models. The computational and implementation
difficulties involved in design of optimal and adaptive controller for higher order linear time invariant
system can also be minimized with the help of reduced order models. Several methods have been developed
for designing of PID controller [6-9]. Recently Evolutionary algorithms have been suggested to improve the
PID tuning, such as those using Genetic Algorithm (GA) [10], Particle Swarm Optimization algorithm
(PSO) [11] and Differential Evolutionary algorithm (DE) [12]. With the advance of computational methods

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Vol 2, No.4, 2012

in the recent times, optimization algorithms are proposed to tune the control parameters in order to find the
optimal performance.

Recently, the Artificial Bee Colony Algorithm (ABC) appeared as promising evolutionary techniques for
handling the optimization problems, which is based on the intelligent foraging behavior of honey bee
swarm, proposed by Karaboga in 2005[13-14]. This swarm algorithm is very simple and flexible when
compared to the other existing swarm based algorithms. It can be used for solving uni-model and multi-
model numerical optimization problems. This algorithm uses only common control parameters such as
colony size and maximum cycle number. It is a population based search procedure and can be modified
using the artificial bees with time and the aim of the bees is to discover the places of food sources with high
nectar amount and finally choose source with the highest nectar amount among the other resources.

In this paper, controller design of a higher order discrete system is presented employing process reduction
approach. The original higher order discrete system is reduced to a lower order model employing ABC
Algorithm based on the minimization of the integral square error (ISE) between the transient responses of
original higher order and the reduced order model pertaining to unit step input. Then a proportional integral
derivative (PID) controller is designed for reduced order model. The parameters of the PID controller are
tuned by using the same error minimization technique employing ABC. The performance of the designed
PID controller is verified by connecting with original higher order discrete system to get the desired
specifications.

II. DESCRIPTION OF THE PROBLEM

A. Model order reduction:

Consider an order linear time invariant discrete system represented by

( ) ∑
( ) ( )
( ) ∑

The objective is to find an order model that has a transfer function( )

( ) ∑
( ) ( )
( ) ∑

( ) ( ) ( ) ( ) are scalar constants. The derivation
of successful reduced order model coefficients for the original higher order model is done by minimizing
the error index ‘E’, known as ISE, employing ABC and is given by [15]:

∫ ( ) ( ) ( )

Where ( ) and ( ) are the unit step response of original and reduced order systems.

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B. Controller design:
All the methods for design of a controller by process reduction technique involve the following steps:

Step-1: Determine the lower order model to a given original higher order discrete system by minimizing the
integral square error (E).

Step-2: Design a PID controller for the reduced order model. The parameters of the PID controller are
optimized using the same error minimization technique between the desired and actual response pertaining
to a unit step input, Employing ABC Algorithm.

Step-3: Test the designed PID controller for the reduced order model for which the PID controller has been
designed.

Step-4: Test the designed PID controller for the original higher order model.

III. PROPORTIONAL INTEGRAL DERIVATIVE (PID) CONTROLLER

A Proportional-Integral-Derivative controller (PID controller) is a generic control loop feedback
mechanism (controller) widely used in industrial control systems. The basic structure of conventional
feedback control system is shown in fig (1). The PID controller compares the measured process value
with a reference set point value the difference (or) error ( ) is then processed to calculate a new process
input . This input will try to adjust the measured process value back to the desired point. A PID
controller calculates an ‘error’ value as the difference between measured process variable ( ) and a desired
set point( ). The error ‘e’ is defined as

The controller attempts to minimize the error by adjusting the process control inputs. In the absence of
knowledge of the underlying process, PID controllers are the best controllers. However, for best
performance, the PID parameters used in the calculation must be according to the nature of the system
while the design is generic. The parameters depend on the specific system. The block diagram of PID
controller is shown in fig (2).

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Vol 2, No.4, 2012

The PID controller algorithm involves three constant parameters, and is accordingly sometimes called
‘Three-term control’: The Proportional, Integral and Derivative values, denoted by P, I, D. Heuristically,
these values can be interpreted in terms of time: P depends on present error, I on accumulation of past
error, and D is prediction of future errors, based on current rate of change. The weighted sum of these three
actions is used to adjust the process via a control element such as the position of a control value. The PID
controller output ( ) is defined as

( ) ( ) ∫ ( ) ( ) ( )

The PID controller transfer function ( ) is given as:

( )
( )
( )

By using linear transformation in the above equation we get ( ) which is given as:

( ) ( )
( )

Where proportional gain, a tuning parameter; integral gain, a tuning parameter; derivative
gain, a tuning parameter; error between actual output and reference input. By tuning these constants in
the PID controller algorithm, the controller can provide control action designed for specific process
requirements. The response of the controller can be described in terms of the responsiveness of the
controller to an error, the degree to which the controller overshoot signal over shoots, the set point and the
degree of system oscillations.

IV. ARTIFICIAL BEE COLONY (ABC) ALGORITHM:
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Vol 2, No.4, 2012

ABC is a population based optimization algorithm based on intelligent behavior of honey bee swarm [13].
In the ABC algorithm, the foraging bees are classified into three categories; Employed bees, Onlookers and
Scout bees. A bee waiting on the hive for making decision to choose a food source is called an Onlooker
and a bee going to the food source visited by it previously is named an Employed bee. A bee carrying out
random search is called a Scout. The employed bees exploit the food source and they carry the information
about the food source back to the hive and share information with onlookers. Onlooker bees are waiting in
the hive at dance floor for the information to be shared by the employed bees about their discovered food
sources and scouts bees will always be searching for new food sources near the hive. Employed bees share
information about food sources by dancing in the designated dance area inside the hive. The nature of
dance is proportional to the nectar content of food source just exploited by the dancing bee. Onlooker bees
watch the dance and choose a food source according to the probability proportional to the quality of that
food source. Therefore, good food sources attract more onlooker bees compared to bad ones. Whenever a
food source is exploited fully, all the employed bees associated with it abandon the food source and become
scout. Scout bees can be visualized as performing the job of exploration, where as employed and onlooker
bees can be visualized as performing the job of exploitation.

In the ABC algorithm, each food source is a possible solution for the problem under consideration and the
nectar amount of a food source represents the quality of the solution which further represents the fitness
value. The number of food sources is same as the number of employed bees and there is exactly one
employed bee for every food source. At the first step, the ABC generates a randomly distributed initial
population P (C=0) of SN solutions (food sources position), where SN denotes the size of population. Each
solution (food sources) ( ) is a D-dimension vector. Here D is number of optimization
parameters. After initialization, the population of the position (solution) is subjected to repeated cycles,
of the search process of the employed bees, onlookers and scouts. The production of new
food source position is also based on comparison process of food source’s position. However, in the model,
the artificial bees do not use any information in comparison. They randomly select a food source position
and produce a modification on the existing, in their memory as described in Eq.(6) provided that the nectar
amount of the new source is higher than that of the previous one of the bee memorizes the new position and
forgets the old position. Otherwise she keeps the position of the previous one. An onlooker’s bees evaluate
the nectar information taken from all employed bees and choose a food source depending on the probability
value associated with that food source , calculated by the following equation(5):

( )
∑

Where is the fitness value of the solution ‘i’ evaluated by its employed bee, which is proportional to
the nectar amount of food source in the position and SN. In this way, the employed bees exchange their
information with the onlookers. In order to produce a new food position from the old one, the ABC uses
following expression (6):

( ) ( )

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Vol 2, No.4, 2012

Where ( ) and ( ) are randomly chosen indexes. Although ‘k’ and ‘l’ are
determined randomly, it has to be different from . is a random number between[-1, 1]. It
controls the production of neighbor food source position around and the modification represents the
comparison of the neighbor food positions visualized by the bee. Equation (6) shows that as the difference
between the parameters of the decreases, the perturbation on the position decreases too.
Thus, as the search approaches to the optimum solution in the search space, the step length is adaptively
reduced. If its new fitness value is better than the best fitness value achieved so far then the bee moves to
this new food source abandoning the old one, otherwise it remains in its old food source. When all
employed bees have finished this process, they share the fitness information with the onlookers, each of
which selects a food source according to probability given in Eq. (5). With this scheme, good food sources
will get more onlookers than the bad ones. Each bee will search for better food source around neighborhood
path for a certain number of cycles (limit), and if the fitness value will not improve then that bee becomes
scout and discover a new food source to be replaced with . This operation can be defined as

( ) ( ) ( ).

V. FLOW CHART FOR ABC ALGORITHM

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Vol 2, No.4, 2012

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Vol 2, No.4, 2012

VI. NUMERICAL EXAMPLES

Consider the transfer function of plant from references [3] as:

( )
( )
( )
( )

For which a controller is to design to get desired output.

Application of ABC for model order reduction:

To obtain lower order model for higher order model ABC is employed. The objective function ‘E’ defined
as an integral squared error of difference between the responses given by equation (3) is minimized by
ABC. In the present study, a population size of SN=50, and maximum number of cycles ( )
have been used.

Finally the successful reduced order model employing ABC technique is obtained as given in equation
(9):

( ) ( )

The unit step response of original and reduced systems is shown in fig (3). It can be seen that the
steady state responses of proposed reduced order model is exactly matching with that of original
model. Also the transient response of proposed reduced model by ABC is very close to that of
original model.

Applications of ABC for PID controller design:

In this study, the PID controller has been designed employing process reduction approach. The
original higher order discrete system given by equation (8) is reduced to lower order model
employing ABC technique given by equation (9). Then the PID controller is designed for lower
order model. The parameters of PID controller are tuned by using same error minimization
technique employing ABC as explained in section IV the optimized PID controller parameters
are: ; The transfer function of the designed PID
controller is as follows:

( )

The unit step responses of the original, reduced system with and without PID controller are
shown in fig (4) and fig (5). It is obvious from the fig (5). that the design of PID controller using

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Vol 2, No.4, 2012

the proposed ABC optimization technique helps to obtain the designer’s specification in transient
as well as steady state responses for the original system.
Step response
1.5

1
Amplitude

0.5 Original 8th order discrete model
Reduced 2nd order discrete model by ABC

0
0 10 20 30 40 50 60 70 80 90 100
Time(sec)
Fig(3) Step Response of Original system and Reduced model

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Vol 2, No.4, 2012

Step response
1.5

1
Amplitude

0.5
Reduced 2nd order system with out PID controller
Reduced 2nd order system with PID controller
0
0 20 40 60 80 100
Time(sec)
Fig(4) step response of reduced with PID controller

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Vol 2, No.4, 2012

Step response
1.5

1
Amplitude

0.5 Original 8th order system with out PID controller
Original 8th order system with PID controller

0
0 20 40 60 80 100
Time(sec)
Fig(5) step response of original system with PID controller

VII. CONCLUSION
The proposed model reduction method uses the swarm intelligence and population based Artificial Bee
Colony algorithm in its procedure to formulate the stable and approximate reduced order model for the
original higher order discrete-time systems. The quality of a formulated reduced order model is judged by
designing the discrete PID controller. PID controller of the formulated reduced order system efficiently
controls the original higher order system. This approach minimizes the complexity involved in direct design
of PID controller. The algorithm is simple to implement and computer oriented.

REFERENCES

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[4] C. P. Therapos, “A direct method for model order reduction of discrete systems “, Journal of Franklin Institute,
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