Fuzzy logic based mrac for a second order system

International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING
6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME
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ISSN 0976 – 6545(Print)
ISSN 0976 – 6553(Online)
Volume 4, Issue 2, March – April (2013), pp. 13-24 IJEET
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FUZZY LOGIC BASED MRAC FOR A SECOND ORDER SYSTEM

1 2
Rajiv Ranjan , Dr. Pankaj Rai
1
(Assistant Manager (Projects)/Modernization & Monitoring, SAIL, Bokaro Steel Plant, India
2
(Head of Deptt., Deptt. Of Electrical Engineering, BIT Sindri, Dhanbad ,India

ABSTRACT

In this paper a fuzzy logic approach for model reference adaptive control (MRAC)
scheme for MIT rule in presence of first order and second order noise has been discussed.
This noise has been applied to the second order system. Simulation is done in MATLAB-
Simulink and the results are compared for varying adaptation mechanisms due to variation in
adaptation gain with and without noise. The result shows that system is stable.

Keywords: Adaptive Control, MRAC (Model Reference Adaptive Controller), Adaptation
Gain, MIT rule, Fuzzy Logic, Noise

1. INTRODUCTION

Robustness in Model reference adaptive Scheme is established for bounded
disturbance and unmodeled dynamics. Adaptive controller without having robustness
property may go unstable in the presence of bounded disturbance and unmodeled dynamics.
Fuzzy logic controller gives the better response even in the present of bounded disturbance
and unmodeled dynamics.
Model reference adaptive controller has been designed to control the nonlinear
system. MRAC forces the plant to follow the reference model irrespective of plant parameter
variations to decrease the error between reference model and plant to zero[2]. Effect of
adaption gain on system performance for MRAC using MIT rule for first order system[3] and
for second order system[4] has been discussed. Comparison of performance using MIT rule
& and Lyapunov rule for second order system for different value of adaptation gain is
discussed [1]. Even in the presence of bounded disturbance and unmodeled dynamics system
show stability in chosen range of adaptation gain[5].

13


Now a days, Fuzzy logic control is used for nonlinear, higher order and time delay
system[6]. Fuzzy logic is widely used as a tool for uncertain, imprecise decision making
problems [7].Fuzzy logic controller located in error path of MRAC without disturbances has
been discussed [8].
In this paper adaptive controller for second order system using MIT rule in the
presence of first order and second order bounded disturbance and unmodeled dynamics has
been discussed first and then fuzzy logic controller has been applied. Simulation has been
done for different value of adaptation gain in MATLAB with fuzzy logic control and without
fuzzy logic control and accordingly performance analysis has been discussed for MIT rule
for second order system in the presence of bounded disturbance and unmodeled dynamics.

2. MODEL REFERENCE ADAPTIVE CONTROL

Model reference adaptive controller is shown in Fig. 1. The basic principle of this
adaptive controller is to build a reference model that specifies the desired output of
the controller, and then the adaptation law adjusts the unknown parameters of the plant so
that the tracking error converges to zero [6]

Figure. 1

3. MIT RULE

There are different methods for designing such controller. While designing an MRAC
using the MIT rule, the reference model, the controller structure and the tuning gains for the
adjustment mechanism is selected. MRAC begins by defining the tracking error, e, which is
difference between the plant output and the reference model output:

system model e=y(p) −y(m) (1)

The cost function or loss function is defined as
F (θ) = e2 / 2 (2)

The parameter θ is adjusted in such a way that the loss function is minimized. Therefore, it is
reasonable to change the parameter in the direction of the negative gradient of F, i.e

14


1 2 (3)
J (θ ) = e (θ )
2

dθ δJ δe
= −γ = −γe (4)
dt δθ δθ

– Change in γ is proportional to negative gradient of J

J (θ ) = e(θ )
dθ δe (5)
= −γ sign(e)
dt δθ
 1, e > 0

where sign(e) =  0, e = 0
− 1, e < 0


From cost function and MIT rule, control law can be designed.

4. MATHEMATICAL MODELLING IN PRESENCE OF BOUNDED AND
UNMODELED DYNAMICS

Model Reference Adaptive Control Scheme is applied to a second order system using
MIT rule has been discussed [3] [4]. It is a well known fact that an under damped second
order system is oscillatory in nature. If oscillations are not decaying in a limited time period,
they may cause system instability. So, for stable operation, maximum overshoot must be as
low as possible (ideally zero).
In this section mathematical modeling of model reference adaptive control (MRAC) scheme
for MIT rule in presence of first order and second order noise has been discussed

Considering a Plant: = -a - by + bu (6)

Consider the first order disturbance is = - +

Where is the output of plant (second order under damped system) and u is the controller
output or manipulated variable.

Similarly the reference model is described by:

= - - y+ r (7)

Where is the output of reference model (second order critically damped system) and r is
the reference input (unit step input).

15


Let the controller be described by the law:

u = θ1 r − θ 2 y p (8)
e = y p − y m = G p Gd u − Gm r (9)

 b  k 
y p = G p Gd u =  2  (θ 1 r − θ 2 y p )
 s + as + b  s + k 
bkθ 1
yp = 2 r
( s + as + b)( s + k ) + bkθ 2

bkθ 1
e= 2
r − Gm r
( s + as + b)( s + k ) + bkθ 2
∂e bk
= 2 r
∂θ 1 ( s + as + b)( s + k ) + bkθ 2
∂e b 2 k 2θ 1
=− 2 r
∂θ 2 [( s + as + b)( s + k ) + bkθ 2 ] 2
bk
=− 2
yp
( s + as + b)( s + k ) + bkθ 2

If reference model is close to plant, can approximate:

( s 2 + as + b)(s + k ) + bkθ 2 ≈ s 2 + a m s + bm
bk ≈ b (10)
∂e bm
= b / bm 2 uc
∂θ 1 s + a m s + bm
(11)
∂e bm
= −b / b m 2 y plant
∂θ 2 s + a m s + bm

Controller parameter are chosen as θ1 = bm /b and θ 2 = ( b − bm )/b

Using MIT

dθ 1 ∂e  bm  (12)
= −γ e = −γ  2 
 s + a s + b u c e
dt ∂θ 1  m m 
dθ 2 ∂e  bm 
= −γ e =γ 2
s +a s+b y plant e

(13)
dt ∂θ 2  m m 

Where γ = γ ' x b / bm = Adaption gain
Considering a =10, b = 25 and am =10 , bm = 1250

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5. MIT RULE IN PRESENT OF BOUNDED DISTRUBANCE AND UNMODELED
DYNAMICS

Consider the first order disturbance:

1
Gd =
s +1

Time response for different value of adaption gain for MIT rule in presence of first order
disturbance is given below:

Figure 2 Figure 3

Figure 4 Figure 5

17


Simulation results with different value of adaptation gain for MIT rule in presence of first
order bounded and unmodeled dynamics is summarized below:

Without any In presence of first order bounded and unmodeled
controller dynamics
γ =0.1 γ =2 γ =4 γ =5
Maximum 61% 0 27% 30% 35%
Overshoot
(%)
Undershoot 40% 0 15% 18% 21%
(%)
Settling Time 1.5 26 8 6 4
(second)

In the presence of first order disturbance the adaptation gain increases the overshoot and
undershoot with decrease in settling time. This overshoot and undershoot are due to the first
order bounded and unmodeled dynamics. It shows that even in the presence of first order
bounded and unmodeled dynamics, system is stable.

Consider the second order disturbance:

25
Gd = 2
s + 30s + 25

Time response for different value of adaption gain for MIT rule in presence of first order
disturbance is given below:

Figure 6 Figure 7

18


Figure 8 Figure 9

Simulation results with different value of adaptation gain for MIT rule in presence of second

Without any In presence of second order bounded and unmodeled
controller dynamics
γ =0.1 γ =2 γ =4 γ =5
Maximum 61% 0 35% 40% 45%
Overshoot
(%)
Undershoot 40% 0 10% 15% 20%
(%)
Settling Time 1.5 30 12 10 7
(second)

In the presence of first order disturbance the adaptation gain increase the overshoot and
undershoot with decrease in settling time. This overshoot and undershoot are due to the
second order bounded and unmodeled dynamics. It shows that even in the presence of first
order bounded and unmodeled dynamics, system is stable.

6. FUZZY LOGIC CONTROLLER

The structure of fuzzy logic consists of three functional block as fuzzyfication
interface, fuzzy interface engine and defuzzyfication.. Design of fuzzy logic controller
involves the appropriate of a parameter set. Parameter set includes the input and outputs of
fuzzy logic controller, the number of linguistic terms and the respective membership
functions for each linguistic variable, the interface mechanism, the rule and the fuzzifiaction
and defuzzyfication method.
The fuzzy controller implements a rule base made set of IF-THEN type rule. An example of
IF-THEN rule is following.
IF e is negative big(NB) and de is negative big(NB) THEN u is positive big(PB).
In this case we have considered five number of triangular membership functions. Rule table
is shown in table 1.

19


Table no: 1
de
e NB NS Z PS PB
NB PB PB PB PS Z
NS PB PB PS Z NS
Z PB PS Z NS NB
PS PS Z NS NB NB
PB Z NS NB NB NB

7. FUZZY ADAPTIVE CONTROLLER

To achieve better time response fuzzy logic controller is applied with adaptive
controller. Proposed method improves the rise time, settling time, overshoot and steady state
error. Fuzzy logic controller is located in error path of MRAC in presence of first order and
second bounded and unmodeled dynamics to minimize the error. Proposed adaptive MRAC
is shown in figure 10.
ym
e
Reference Model

u Plant Noise
Controller

r Fuzzy
Logic

Figure 10

10. FUZZY LOGIC CONTROL WITH MIT RULE IN PRESENT OF BOUNDED
DISTRUBANCE AND UNMODELED DYNAMICS

Consider the first order disturbance:

1
Gd =
s +1
Time response for different value of adaption gain for MIT rule with fuzzy logic controller in
presence of first order disturbance is given below:

20


Figure 11 Figure 12

Figure 13 Figure 14

Simulation results with different value of adaptation gain for MIT rule in presence of first

With Fuzzy Logic
γ =0.1 γ =2 γ =4 γ =5
Maximum 0 15% 20% 26%
Overshoot
(%)
Undershoot 0 3% 6% 8%
(%)
Settling Time 15 7 5 3
(second)

21


With fuzzy logic controller, it is observed that overshoot, undershoot and settling time
considerably has been improved even in presence of first order bounded and unmodeled
dynamics, showing stability of the system.

Consider the second order disturbance:

25
Gd = 2
s + 30s + 25

Time response for different value of adaption gain for MIT rule with fuzzy logic controller in
presence of second order disturbance is given below:

Figure 15 Figure 16

Figure 17 Figure 18

22


Simulation results with different value of adaptation gain for MIT rule in presence of second

With Fuzzy Logic
γ =0.1 γ =2 γ =4 γ =5
Maximum 0 16% 20% 24%
Overshoot
(%)
Undershoot 0 3% 4% 6%
(%)
Settling Time 17 8 6 4
(second)

With fuzzy logic controller, it is observed that overshoot, undershoot and settling time
considerably has been improved even in presence of second order bounded and unmodeled
dynamics, showing stability of the system.

8. CONCLUSION

The use of Fuzzy logic controller gives that better result as compared to conventional
Model Reference adaptive Controller. Comparison of result between conventional MRAC
and with fuzzy logic controller shows considerable improvement in overshoot, undershoot
and settling time.
Time response is studied in the presence of first order & second order bounded and
unmodeled dynamics using MIT rule with varying the adaptation gain using fuzzy controller.
It is observed that, disturbance added in the conventional MRAC has some oscillations at the
peak of signal is considered as a random noise. This noise has been reduced considerably by
the use of fuzzy logic controller. It can be concluded that fuzzy logic controller shows better
response and can be considered good enough for second order system in presence of first
order & second order bounded and unmodeled dynamics.

REFERENCES

[1] R.Ranjan and Dr. Pankaj rai “Performance Analysis of a Second Order System using
MRAC” International Journal of Electrical Engineering & Technology, Volume 3, Issue 3,
October - December (2012), pp. 110-120. Published by IAEME
[2] Slontine and Li, “Applied Nonlinear Control”, p 312-328, ©1991 by Prentice Hall
International Inc
[3] P.Swarnkar, S.Jain and R. Nema “Effect of adaptation gain on system performance
for model reference adaptive control scheme using MIT rule” World Academy of science,
engineering and technology, vol.70, pp 621-626, Oct’2010
[4] P.Swarnkar, S.Jain and R. Nema “Effect of adaptation gain in model reference
adaptive controlled second order system” ETSR-Engineering, Technology and applied
science research,, vol.1, no,-3 pp 70-75, 2011
[5] R.Ranjan and Dr. Pankaj rai “ROBUST MODEL REFERENCE ADAPTIVE
CONTROL FOR A SECOND ORDER SYSTEM.’ International Journal of Electrical
Engineering & Technology, Volume 4, Issue 1, (Jan-Feb 2013), Page no. 9-18. Published by
IAEME.

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[6] S.R.Vaishnav, Z.J.Khan, “Design and Performance of PID and Fuzzy logic controller
with smaller rule base set for highr order system” World Congress on engineering and
Coputer Science, 2007
[7] M.Muruganandam, M. Madheshwaran “Modeling and simulation of modified Fuzzy
logic controller for various type of DC motor drives” International conference for control,
automation, communication and energy conservation, 2009
[8] S.Z.Moussavi, M. Alasvandi & Sh, Javadi “Speed Control of Permanent Magnet DC
motor by using combination of adaptive controller and fuzzy controller” International Journal
of computer application , Volume 25, No-20 August 2012.
[9] Jasmin Velagic, Amar Galijasevic," Design of fuzzy logic control of permanent
magnet DC motor under real constraints and disturbances", IEEE International Symposium
on Intelligent Control, 2009.
[10] A. Suresh kumar, M. Subba Rao, Y.S.Kishore Babu, "Model reference linear adaptive
control of DC motor Using Fuzzy Controller", IEEE Region 10 Conference, 2008.
[11] VenkataRamesh.Edara, B.Amarendra Reddy, Srikanth Monangi and M.Vimala,
“Analytical Structures For Fuzzy Pid Controllers and Applications” International Journal of
Electrical Engineering & Technology (IJEET), Volume 1, Issue 1, 2010, pp. 1 - 17, Published
by IAEME.
[12] M.Gowrisankar and Dr. A. Nirmalkumar, Srikanth Monangi and M.Vimala,
“Implementation & Simulation of Fuzzy Logic Controllers for the Speed Control Of
Induction Motor and Performance Evaluation of Certain Membership Functions”
International Journal of Electrical Engineering & Technology (IJEET), Volume 2, Issue 1,
2011, pp. 25 - 35, Published by IAEME.

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Fuzzy logic based mrac for a second order system

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