This document compares several classical tuning methods for PID controllers, including both closed-loop and open-loop approaches. It describes the Ziegler-Nichols, Tyreus-Luyben, Damped Oscillation, C-H-R, Cohen-Coon, Ciancone-Marlin, and Minimum Error Integral tuning methods. The document aims to compare the performance and robustness of these tuning methods through simulation of first, second, and third-order processes. It also describes developing a GUI to automatically compare the tuning methods for a given process model.

1. PID Controller Tuning
Comparison of classical tuning methods
By Ahmad Taan
1
University of Jordan, Department of Mechatronics Engineering, 2014

2. Content
 Introduction
 Objectives
 Closed-loop Methods
 Ziegler-Nichols Closed-loop
 Tyreus-Luyben
 Damped Oscillation
 Open-loop Methods
 Ziegler-Nichols Open-loop
 C-H-R
 Cohen-Coon
 Ciancone-Marlin
 Minimum Error Integral
 Simulation and Results
 GUI Description
June 16, 2015 2University of Jordan, Department of Mechatronics Engineering, 2014

3. Introduction
 PID tuning is to find the optimum Kp, Ki and Kd for the controller.
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Control objective > Setpoint tracking, Disturbance rejection
Actions > Instantaneous proportional action, Reset integral action, Rate derivative
action
Optimum criteria > Depends on application and system requirements

4. Introduction
 Conceptual real-world example
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Driver
(PID)
Car mechanism
(Process)
Crosswind
Front wheels
angle Car position
Driver’s eyes
(Feedback)
Desired position

5. Introduction
 PID configuration
𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑙𝑒𝑟 𝑜𝑢𝑡𝑝𝑢𝑡 = 𝐾 𝑝 𝑒(𝑡) + 𝐾𝑖 𝑒(𝑡)𝑑𝑡 + 𝐾 𝑑
d𝑒(𝑡)
𝑑𝑡
= 𝐾𝑐 × (1 +
1
𝜏𝑖
𝑒(𝑡)𝑑𝑡 + 𝜏 𝑑
d𝑒(𝑡)
𝑑𝑡
)
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𝐾 𝑝 𝑒(𝑡)
𝐾𝑖 𝑒(𝑡)𝑑𝑡
𝐾 𝑑
d𝑒(𝑡)
𝑑𝑡
SP
PV
Controller outpute(t)

6. Introduction
 Many tuning methods have been proposed for PID controllers each of which
has its advantages and disadvantages. So, no one can be considered the best
for all purposes.
 Closed-loop methods tune the PID while it is attached to the loop while in
open-loop methods the process is estimated using a FOPDT model
 A comparison of the most popular methods is to be done
 Simulation will be implemented for 1st, 2nd and 3rd-order processes, some of
which are lag-dominant and the others are dead-time dominant.
 IAE as criterion (which adds up the time and amplitude weight of the error)
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7. Objectives
 Compare studied tuning methods for performance and robustness
 Develop a GUI to do the comparison automatically for a given process model
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8. Closed-loop methods
 Ziegler-Nichols Closed-loop
 Tyreus-Luyben
 Damped Oscillation
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PID Process
D
C PV
Feedback
SP
Tuning

9. Open-loop methods
 Ziegler-Nichols Open-loop
 C-H-R
 Cohen-Coon
 Ciancone-Marlin
 Minimum Error Integral
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PID Process
D
PV
Tuning

10. Ziegler-Nichols Closed-loop
 ¼ decay ratio as design criterion (stability condition)
 Trial-and-error procedure to find 𝑲 𝒖 and 𝑷 𝒖
 Drives the process into marginal stability
 Performs well when 𝝉 𝒎 ≥ 𝟐𝒕 𝒅 (lag dominant)
 Performs very poorly for 𝒕 𝒅 > 𝟐𝝉 𝒎 (dead-time dominant)
 Fast recovery from disturbance but leads to oscillatory response
 Not applicable to open-loop-unstable processes
 Some processes do not have ultimate gain
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11. Ziegler-Nichols Closed-loop
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Controller 𝐾𝑐 𝜏𝑖 𝜏 𝑑
P 0.5𝐾 𝑢 - -
PI 0.45𝐾 𝑢 0.83𝑃𝑢 -
PID 0.6𝐾 𝑢 0.5𝑃𝑢 0.125𝑃𝑢
 Procedure:
 Set 𝐾𝑖 and 𝐾 𝑑 to 0
 Increase 𝐾 𝑝 till sustained oscillation and find 𝐾 𝑢 and 𝑃𝑢
 Use the correlations in the table below

12. Tyreus-Luyben
 An improvement for Ziegler-Nichols closed-loop to make response less
oscillatory
 More robust to imprecise model
 Gives better disturbance response
 Procedure:
 Same procedure as Ziegler-Nichols closed-loop
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Controller 𝐾𝑐 𝜏𝑖 𝜏 𝑑
PI 0.45𝐾 𝑢 2.2𝑃𝑢 -
PID 0.313𝐾 𝑢 2.2𝑃𝑢 0.16𝑃𝑢

13. Damped Oscillation
 Another improvement for Ziegler-Nichols closed-loop
 Solves the problem of marginal stability
 Can be used with open-loop-unstable processes
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0
0.2
0.4
0.6
0.8
1
1.2
0 10 20 30 40 50 60 70 80
4:1

14. Damped Oscillation
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Controller 𝐾𝑐 𝜏𝑖 𝜏 𝑑
PI 𝑎𝑑𝑗𝑢𝑠𝑡𝑒𝑑 𝑃𝑑/6 -
PID 𝑎𝑑𝑗𝑢𝑠𝑡𝑒𝑑 𝑃𝑑/6 𝑃𝑑/1.5
 Procedure:[1]
 Set 𝐾𝑖 and 𝐾 𝑑 to 0
 Increase 𝐾 𝑝 till ¼ damping ratio is maintained and find 𝑃𝑑 only
 Use the correlations in the table below to find 𝜏𝑖 and 𝜏 𝑑
 Adjust 𝐾 𝑝 till ¼ damping ratio is maintained again
[1] Lipták, Béla G., and Kriszta Venczel. Instrument Engineers' Handbook: Process Control 4thed, Volume Two.

15. Ziegler-Nichols Open-loop
 ¼ decay ratio as design criterion
 Performs well when 𝜏 𝑚 ≥ 2𝑡 𝑑 (lag dominant)
 Performs very poorly for 𝑡 𝑑 > 2𝜏 𝑚 (dead-time dominant)
 Fast recovery from disturbance but leads to oscillatory response
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16. Ziegler-Nichols Open-loop
 Procedure:
 The process dynamics is modeled by a first order plus dead time model
𝐺 𝑚 𝑠 =
𝐾 𝑚 𝑒−𝑡 𝑑 𝑠
𝜏 𝑚 𝑠 + 1
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-0.5
0
0.5
1
1.5
2
2.5

17. Ziegler-Nichols Open-loop
 PID parameters are calculated from the table below
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Controller 𝐾𝑐 𝜏𝑖 𝜏 𝑑
P 1
𝐾 𝑚
𝜏 𝑚
𝑡 𝑑
- -
PI 0.9
𝐾 𝑚
𝜏 𝑚
𝑡 𝑑
𝑡 𝑑
0.3
-
PID 1.2
𝐾 𝑚
𝜏 𝑚
𝑡 𝑑
2𝑡 𝑑 0.5𝑡 𝑑

18. C-H-R
 A modification of Ziegler-Nichols Open-loop
 Aims to find the “quickest response with 0% overshoot” or “quickest
response with 20% overshoot”
 Tuning for setpoint responses differs from load disturbance responses
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19. C-H-R
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Setpoint
Controller 𝐾𝑐 𝜏𝑖 𝜏 𝑑
0% overshoot
P 0.3
𝐾 𝑚
𝜏 𝑚
𝑡 𝑑
- -
PI 0.35
𝐾 𝑚
𝜏 𝑚
𝑡 𝑑
1.2𝜏 𝑚 -
PID 0.6
𝐾 𝑚
𝜏 𝑚
𝑡 𝑑
𝜏 𝑚 0.5𝑡 𝑑
Disturbance
P 0.3
𝐾 𝑚
𝜏 𝑚
𝑡 𝑑
- -
PI 0.6
𝐾 𝑚
𝜏 𝑚
𝑡 𝑑
4𝑡 𝑑 -
PID 0.95
𝐾 𝑚
𝜏 𝑚
𝑡 𝑑
2.4𝑡 𝑑 0.42𝑡 𝑑
𝐾𝑐 𝜏𝑖 𝜏 𝑑
20% overshoot
0.7
𝐾 𝑚
𝜏 𝑚
𝑡 𝑑
- -
0.6
𝐾 𝑚
𝜏 𝑚
𝑡 𝑑
𝜏 𝑚 -
0.95
𝐾 𝑚
𝜏 𝑚
𝑡 𝑑
1.4𝜏 𝑚 0.47𝑡 𝑑
0.7
𝐾 𝑚
𝜏 𝑚
𝑡 𝑑
- -
0.7
𝐾 𝑚
𝜏 𝑚
𝑡 𝑑
2.3𝑡 𝑑 -
1.2
𝐾 𝑚
𝜏 𝑚
𝑡 𝑑
2𝑡 𝑑 0.42𝑡 𝑑
 Procedure:
 Same as Ziegler-Nichols Open-loop

20. Cohen-Coon
 Second in popularity after Ziegler-Nichols tuning rules
 ¼ decay ratio has considered as design criterion for this method
 More robust
 Applicable to wider range of
𝒕 𝒅
𝝉
(i.e. 𝑡 𝑑 > 2𝜏)
 PD rules available
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21. Cohen-Coon
 Procedure:[1]
 The process reaction curve is obtained by an open loop test and the FOPDT
model is estimated as follows:
𝜏 𝑚 =
3
2
𝑡2 − 𝑡1
𝑡 𝑑 = 𝑡2 − 𝜏 𝑚
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-0.5
0
0.5
1
1.5
2
2.5
[1] Smith,C.A., A.B. Copripio; “Principles and Practice of Automatic Process Control”, John Wiley & Sons,1985

22. Cohen-Coon
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Controller 𝐾𝑐 𝜏𝑖 𝜏 𝑑
P
1
𝐾 𝑚
𝜏 𝑚
𝑡 𝑑
(1 +
𝑡 𝑑
3𝜏 𝑚
) - -
PI
1
𝐾 𝑚
𝜏 𝑚
𝑡 𝑑
(0.9 +
𝑡 𝑑
12𝜏 𝑚
) 𝑡 𝑑
30 +
3𝑡 𝑑
𝜏 𝑚
9 +
20𝑡 𝑑
𝜏 𝑚
-
PD
1
𝐾 𝑚
𝜏 𝑚
𝑡 𝑑
(1.25 +
𝑡 𝑑
6𝜏 𝑚
) - 𝑡 𝑑
6 −
2𝑡 𝑑
𝜏 𝑚
22 +
3𝑡 𝑑
𝜏 𝑚
PID
1
𝐾 𝑚
𝜏 𝑚
𝑡 𝑑
(1.33 +
𝑡 𝑑
4𝜏 𝑚
) 𝑡 𝑑
32 +
6𝑡 𝑑
𝜏 𝑚
13 +
8𝑡 𝑑
𝜏 𝑚
𝑡 𝑑
4
11 +
2𝑡 𝑑
𝜏 𝑚
 PID parameters are calculated from the table

23. Ciancone-Marlin
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 Design criteria:
 Minimization of IAE
 Assumption of ±25% change in the process model parameters
 A set of graphs are used for the tuning
 Tuning for setpoint responses differs from load disturbance responses

24. Ciancone-Marlin
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 Procedure:
 Estimate the process with FOPDT as for Cohen-Coon method
 Calculate the ratio
𝑡 𝑑
𝑡 𝑑+𝜏 𝑚
 From the appropriate graph determine the values (𝐾𝑐 𝐾 𝑚,
𝜏 𝑖
𝑡 𝑑+𝜏 𝑚
,
𝜏 𝑑
𝑡 𝑑+𝜏 𝑚
)
 Do the calculation to find the PID parameters

25. Ciancone-Marlin
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0
0.5
1
1.5
0 0.5 1
0.5
0.7
0.9
1.1
1.3
1.5
0 0.5 1
0
0.2
0.4
0.6
0.8
1
0 0.5 1
0
0.2
0.4
0.6
0.8
1
0 0.5 1
setpointDisturbance

26. Ciancone-Marlin
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0
0.5
1
1.5
2
0 0.5 1
0
0.5
1
1.5
2
0 0.5 1
0
0.2
0.4
0.6
0.8
0 0.5 1
0
0.2
0.4
0.6
0.8
1
0 0.5 1
0
0.05
0.1
0.15
0.2
0.25
0 0.5 1
0
0.05
0.1
0.15
0.2
0.25
0 0.5 1
setpointDisturbance

27. Minimum Error Integral
June 16, 2015 27University of Jordan, Department of Mechatronics Engineering, 2014
 Considers the entire closed loop response not like the ¼-decay tuning methods which
considers only the first two peaks
 Less oscillations in response than ¼-decay
 Performs well when 𝝉 𝒎 ≥ 𝟐𝒕 𝒅 (lag dominant)
 Performs very poorly for 𝒕 𝒅 > 𝝉 𝒎 (dead-time dominant)
 Tuning for setpoint responses differs from load disturbance responses
 Different error integrals can be used (IAE, ISE, ITAE, ITSE)
𝐼𝐴𝐸 =
0
∞
𝑒(𝑡) 𝑑𝑡 , 𝐼𝑆𝐸 =
0
∞
𝑒(𝑡)2
𝑑𝑡 , 𝐼𝑇𝐴𝐸 =
0
∞
𝑡 𝑒(𝑡) 𝑑𝑡 , 𝐼𝑇𝑆𝐸 =
0
∞
𝑡𝑒(𝑡)2
𝑑𝑡

28. Minimum Error Integral
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 Procedure:
 Estimate the process with FOPDT as for Cohen-Coon method
 Use the appropriate table to find the PID parameters

29. Minimum Error Integral
June 16, 2015 29University of Jordan, Department of Mechatronics Engineering, 2014
Error integral IAE ITAE
PI Controller
𝐾𝑐 =
𝑎1
𝐾 𝑚
(
𝑡 𝑑
𝜏 𝑚
) 𝑏1
𝑎1 = 0.758
𝑏1 = −0.861
𝑎1 = 0.586
𝑏1 = −0.916
𝜏𝑖 =
𝜏 𝑚
𝑎2 + 𝑏2(
𝑡 𝑑
𝜏 𝑚
)
𝑎2 = 1.02
𝑏2 = −0.323
𝑎2 = 1.03
𝑏2 = −0.165
PID Controller
𝐾𝑐 =
𝑎1
𝐾 𝑚
(
𝑡 𝑑
𝜏 𝑚
) 𝑏1
𝑎1 = 1.086
𝑏1 = −0.869
𝑎1 = 0.965
𝑏1 = −0.855
𝜏𝑖 =
𝜏 𝑚
𝑎2 + 𝑏2(
𝑡 𝑑
𝜏 𝑚
)
𝑎2 = 0.74
𝑏2 = −0.13
𝑎2 = 0.796
𝑏2 = 0.147
𝜏 𝑑 = 𝑎3 𝜏 𝑚(
𝑡 𝑑
𝜏 𝑚
) 𝑏3
𝑎3 = 0.348
𝑏3 = 0.914
𝑎3 = 0.308
𝑏3 = 0.9292
 Setpoint tracking table

30. Minimum Error Integral
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Error integral IST IAE ITAE
P Controller
𝐾𝑐 =
𝑎1
𝐾 𝑚
(
𝑡 𝑑
𝜏 𝑚
) 𝑏1
𝑎1 = 1.411
𝑏1 = −0.917
𝑎1 = 0.902
𝑏1 = −0.985
𝑎1 = 0.49
𝑏1 = −1.084
PI Controller
𝐾𝑐 =
𝑎1
𝐾 𝑚
(
𝑡 𝑑
𝜏 𝑚
) 𝑏1
𝑎1 = 1.305
𝑏1 = −0.959
𝑎1 = 0.984
𝑏1 = −0.986
𝑎1 = 0.859
𝑏1 = 0.977
𝜏𝑖 =
𝜏 𝑚
𝑎2
(
𝑡 𝑑
𝜏 𝑚
) 𝑏2
𝑎2 = 0.492
𝑏2 = 0.739
𝑎2 = 0.608
𝑏2 = 0.707
𝑎2 = 0.674
𝑏2 = 0.68
PID Controller
𝐾𝑐 =
𝑎1
𝐾 𝑚
(
𝑡 𝑑
𝜏 𝑚
) 𝑏1
𝑎1 = 1.495
𝑏1 = 0.945
𝑎1 = 1.435
𝑏1 = −0.921
𝑎1 = 1.357
𝑏1 = −0.947
𝜏𝑖 =
𝜏 𝑚
𝑎2
(
𝑡 𝑑
𝜏 𝑚
) 𝑏2
𝑎2 = 1.101
𝑏2 = 0.771
𝑎2 = 0.878
𝑏2 = 0.749
𝑎2 = 0.842
𝑏2 = 0.738
𝜏 𝑑 = 𝑎3 𝜏 𝑚(
𝑡 𝑑
𝜏 𝑚
) 𝑏3
𝑎3 = 0.56
𝑏3 = 1.006
𝑎3 = 0.482
𝑏3 = 1.137
𝑎3 = 0.381
𝑏3 = 0.995
 Disturbance rejection table

31. Simulation and Results
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 Simulation performed for two purposes:
 Performance Assessment
 Robustness Assessment
 Simulation for two response objectives:
 Set point tracking
 Disturbance rejection

32. Simulation and Results
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 Test cases include processes of:
 Dead-time dominant (𝑡 𝑑 > 2𝜏 𝑚)
 Lag dominant (𝜏 𝑚≥ 2𝑡 𝑑)
 In-between cases
 Complex poles
 Unstable process
1. 𝐺 𝑠 =
1
𝑠+1
2. 𝐺 𝑠 =
1
0.5𝑠+1
𝑒−0.2𝑠
3. 𝐺 𝑠 =
1
0.5+1
𝑒−1.2𝑠
4. 𝐺 𝑠 =
1
30𝑠2+13𝑠+1
5. 𝐺 𝑠 =
1
𝑠2+3𝑠+1
𝑒−0.2𝑠
6. 𝐺 𝑠 =
1
𝑠2+1.8𝑠+1
𝑒−3𝑠
7. 𝐺 𝑠 =
1
25𝑠+1 20𝑠+1 30𝑠+1
8. 𝐺 𝑠 =
2
150𝑠3+95𝑠2+18𝑠+1
𝑒−0.5𝑠
9. 𝐺 𝑠 =
2
2𝑠3+5𝑠2+4𝑠+1
𝑒−4.2𝑠
10. 𝐺 𝑠 =
250
𝑠2+4𝑠+50
11. 𝐺 𝑠 =
7𝑠2+28𝑠+28
10𝑠3−10𝑠2−50𝑠−30

33. Simulation Example (Closed-loop)
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 𝐺 𝑠 =
1
0.5+1
𝑒−1.2𝑠
Method 𝑲 𝒑 𝑲𝒊 𝑲 𝒅
Ziegler-Nichols Closed-
loop
0.63 0.24 0
Tyreus-Luyben 0.44 0.06 0
Damped Oscillation 0.76 0.28 0
Method IAE ITAE ISE
Ziegler-Nichols Closed-
loop
4.287635 21.66082 2.14574
Tyreus-Luyben 16.21587 326.4134 6.600629
Damped Oscillation 3.657051 16.38796 1.930914
Method Overshoot Rise time
Settling
time
Ziegler-Nichols Closed-
loop
0 9.41773 20.10063
Tyreus-Luyben 0 41.5833 78.08328
Damped Oscillation 0 1.14425 17.86827

34. Simulation Example (Open-loop)
June 16, 2015 34University of Jordan, Department of Mechatronics Engineering, 2014
 𝐺 𝑠 =
1
0.5+1
𝑒−1.2𝑠
Method 𝑲 𝒑 𝑲𝒊 𝑲 𝒅
Ziegler-Nichols Open-loop 0.38 0.096 0
C-H-R 0.26 0.50 0
Cohen-Coon 0.46 0.59 0
Ciancone-Marlin 0.65 0.61 0
Minimum Error Integral 0.36 0.19 0
Method IAE ITAE ISE
Ziegler-Nichols Open-loop 10.62439 133.3877 4.672032
C-H-R 2.534889 4.215979 1.916891
Cohen-Coon 2.23463 3.378988 1.687213
Ciancone-Marlin 2.31806 4.337486 1.623838
Minimum Error Integral 5.443972 29.46653 2.827566

35. Robustness Assessment Example
June 16, 2015 35University of Jordan, Department of Mechatronics Engineering, 2014
 𝐺 𝑠 =
1
𝑠2+3𝑠+1
𝑒−0.2𝑠
≫ 𝐺 𝑠 =
1
𝑠2+3.4𝑠+1
𝑒−0.4𝑠
Method 𝑲 𝒑 𝑲𝒊 𝑲 𝒅
Ziegler-Nichols
Closed-loop
7.38 5.13 0
Tyreus-Luyben 5.13 1.35 0
Damped Oscillation 8.26 4.36 0
Method ∆%Overshoot ∆%Rise time
∆%Settling
time
Ziegler-Nichols
Closed-loop
2.53E+46 0.005528
Tyreus-Luyben 0.780894 0.021236 0.222945
Damped Oscillation 7.51E+58 0.002601
Method ∆%IAE ∆%ITAE ∆%ISE
Ziegler-Nichols
Closed-loop
65535 65535 65535
Tyreus-Luyben 0.578426 1.141222 0.534852
Damped Oscillation 65535 65535 65535
---- After process parameters change
___ With original process parameters
 Only Tyreus Luyben method could preserve the
system stability in this example

36. Results
June 16, 2015 36University of Jordan, Department of Mechatronics Engineering, 2014
Method
Example 1 Example 2 Example 3 Example 4 Example 5 Example 6
Set. Dis. Set. Dis. Set. Dis. Set. Dis. Set. Dis. Set. Dis.
ZN-Closed - - 0.445789 0.283633 4.287635 4.173887 - - 2.220379 0.30278 13.41728 13.1761
Tyreus-Luyben - - 1.102981 1.070794 16.21587 15.8735 - - 1.180371 0.735662 50.61003 49.72932
Damped Oscillation - - 0.612071 0.236871 3.657051 3.591137 5.435811 0.227883 2.036804 0.273401 12.38092 12.11599
ZN-Open - - 0.477394 0.283206 10.62439 10.40774 6.652971 0.659678 2.429928 0.313117 16.09085 15.75623
C-H-R - - 0.421681 0.25155 2.534889 9.219109 4.185609 1.19549 1.174634 0.444315 6.268245 14.07367
Cohen-Coon - - 0.903723 0.290855 2.23463 2.054926 6.597632 1.828374 1.629527 0.386198 6.621596 6.228913
Ciancone-Marlin - - 0.595529 0.316686 2.31806 2.235919 10.79177 4.51365 2.417798 1.027116 7.183998 6.603842
Minimum Integral E. - - 0.426224 0.264112 5.443972 3.585999 5.563018 1.75844 1.204237 0.367181 14.60711 10.23431
Method
Example 7 Example 8 Example 9 Example 10 Example 11 Average
Set. Dis. Set. Dis. Set. Dis. Set. Dis. Set. Dis. Set. Dis.
ZN-Closed 121.105 33.93362 24.0696 13.75189 19.49302 38.61412 - - - - 26.434 14.8908
Tyreus-Luyben 82.82336 75.37933 19.84668 36.28508 74.32392 145.8678 - - - - 35.1576 46.42
Damped Oscillation 74.90803 33.03475 18.32106 13.56714 17.76392 34.84851 0.8825 4.247397 2.4965 0.5507 13.849 10.269
ZN-Open 203.0636 48.10066 41.21583 19.02999 20.80098 40.49981 - - - - 37.669 16.8813
C-H-R 71.53518 62.488 15.79547 23.05193 10.29351 35.97429 - - - - 14.026 18.337
Cohen-Coon 82.23544 40.9686 18.73435 17.27418 11.04538 19.81969 - - - - 16.25 11.106
Ciancone-Marlin 72.66559 54.42106 17.36664 24.75492 10.93768 21.3825 - - - - 15.5346 14.4069
Minimum Integral E. 61.47353 37.4164 14.01516 15.94768 17.36168 29.64329 - - - - 15.0118 12.402
 Performance assessment

37. Results
June 16, 2015 37University of Jordan, Department of Mechatronics Engineering, 2014
Method
Example 12 Example 13 Example 14 Average
Set. Dis. Set. Dis. Set. Dis. Set. Dis.
ZN-Closed 0.30377 0.000776 - - 0.485444 0.391874 0.3946 0.1963
Tyreus-Luyben 0.013379 0.003065 0.578426 0.008142 0.027758 0.000149 0.2065 0.003785
Damped Oscillation 0.325173 0.164803 - - 0.322041 0.132218 0.3236 0.1485
ZN-Open 0.283954 0.000466 - - - - 0.283954 0.00466
C-H-R - 0.128355 0.619157 - 0.220264 - 0.4197 0.128355
Cohen-Coon - - - 0.903723 - 0.148872 - 0.52629
Ciancone-Marlin 0.004346 0.012664 0.009255 0.595529 0.01106 0.001862 0.00822 0.20335
Minimum Integral E. 0.293021 - 0.295112 0.426224 0.165632 0.101298 0.2512 0.26376
 Robustness assessment

38. GUI Description
June 16, 2015 38University of Jordan, Department of Mechatronics Engineering, 2014

39. GUI Description
June 16, 2015 39University of Jordan, Department of Mechatronics Engineering, 2014

40. GUI Description
June 16, 2015 40University of Jordan, Department of Mechatronics Engineering, 2014

41. GUI Description
June 16, 2015 41University of Jordan, Department of Mechatronics Engineering, 2014

42. GUI Description
June 16, 2015 42University of Jordan, Department of Mechatronics Engineering, 2014