Polesrootlocus_IISC.pdf

Lecture – 5
Classical Control Overview – III
Dr. Radhakant Padhi
Asst. Professor
Dept. of Aerospace Engineering
Indian Institute of Science - Bangalore

ADVANCED CONTROL SYSTEM DESIGN
Dr. Radhakant Padhi, AE Dept., IISc-Bangalore
2
A Fundamental Problem in
Control Systems
z Poles of open loop transfer function are easy to find and they
do not change with gain variation either.
z Poles of closed loop transfer function, which dictate stability
characteristics, are more difficult to find and change with gain
( ) ( )
( )
( )
1 ( ) ( )
( ) ( )
( ) ( )
( ) ( )
( ) ( )
( )
( ) ( ) ( ) ( )
G H
G H
G H
G H G H
open loop transfer function KG s H s
closed loop transfer function
KG s
T s
KG s H s
let
N s N s
G s and H s
D s D s
KN s D s
T s
D s D s KN s N s
=
=
+
= =
=
+

3
Example
3 2
( 1) ( 3)
Let ( ) , ( )
( 2) ( 4)
( 1)( 3)
Then ( ) ( )
( 2)( 4)
Poles of open loop Transfer Function ( ) ( ) (0, 2, 4)
Now Closed Loop Transfer Function ( )
( 1)( 4)
( )
(6 ) (8 4 ) 3
P
s s
G s H s
s s s
K s s
KG s H s
s s s
KG s H s are
T s
K s s
T s
s K s K s K
+ +
= =
+ +
+ +
=
+ +
− −
+ +
=
+ + + + +
− oles of ( ) are not immediately known; they depends on as well.
System stability and transient response depends on poles of ( )
Root Locus gives vivid picture of the poles of ( ) varies
T s K
T s
T s as K
−
−

4
Vector Representation of Complex
Numbers and Complex Functions
We can conclude that (s + a) is a complex number and can be represented by a
vector drawn from the zero of the function (- a) to the point ‘s’.
s j
σ ω
= + ( )
( )
F s s a a j
σ ω
= + = + +
( )
( ) ( )
zero F s zero s a a
= + = − ( ) 5 2
7 s j
s → +
+
Regular representation:
Alternate representation:

5
Magnitude and Angle of Complex
Functions
1
1
1
1
( )
Let ( ) , ,
( )
The magnitude of ( ) at any point s is
( )
( )
The angle , , of ( ) at any point s is
m
i
i
n
j
j
m
i
i
n
j
i
s z
F s m number of zeros n number of poles
s p
M F s
s z
zero lengths
M
pole lengths
s p
F s
zero
θ
θ
=
=
=
=
+
= − −
+
+
= =
+
=
∏
∏
∏
∏
∏ ∏
1 1
( ) ( )
m n
i j
i j
angles pole angles s z s p
= =
− = ∠ + − ∠ +
∑ ∑ ∑ ∑

6
Example
0
0
0
0 0 0
0
( 1)
( )
( 2)
Vector originating at 1: 20 116.6
of ( )
20
= 116.6 126.9 104.0
5 17
=0.217 114.3
s
F s
s s
M F s
θ
+
=
+
− ∠
∠
− ∠
∴ ∠
∠ − −
∠ −

Root Locus Analysis
Dr. Radhakant Padhi
Asst. Professor

8
Facts
• Introduced by W. R. Evans (1948)
• Graphical representation of the closed loop poles in
s-plane as the system parameter (typically controller
gain) is varied.
• Gives a graphic representation of a system’s stability
characteristics
• Contains both qualitative and quantitative information
• Holds good for higher order systems

9
A Motivating Example
Pole location as a function of gain for
the system.
Ref: N. S. Nise:
Control Systems Engineering,
4th Ed., Wiley, 2004
over damped
under damped
Note: Typically K > 0 for negative feedback systems

10
A Motivating Example
Real parts are same:
• same settling time
As gain increases,
• damping ratio decreases
• %age overshoot
increases
Poles are always negative:
•system is always stable

11
Properties of Root Locus
The closed loop Transfer Function
( )
( )
1 ( ) ( )
Characteristics equation: 1 ( ) ( ) 0
Closed loop poles are solution of characteristics equation.
However, 1 ( ) ( ) 0 is a complex quantity. Henc
KG s
T s
KG s H s
KG s H s
KG s H s
=
+
+ =
+ =
0
0
e, it can be expressed as
( ) ( ) 1 1 (2 1)180 0, 1, 2, 3,...........
The above condition (Evan's condition)can be written as
( ) ( ) 1 (Magnitude criterion)
( ) ( ) (2 1)180 (Angle criterion)
KG s H s k k
KG s H s
KG s H s k
=− = ∠ + = ± ± ±
=
∠ = +

12
Example
Example: Let us consider
a system as in the Figure
and consider two points:
2
2
: 2
2
P j
⎛ ⎞
− + ⎜ ⎟
⎜ ⎟
⎝ ⎠
1 : 2 3
P j
− +
0
If the angle of a complex number is an odd multiple of 180 for an open loop
transfer function, then it is a pole of the closed loop system with
1 1
( ) ( ) ( ) ( )
K
G s H s G s H s
= =
Fact :

13
Example
1 1 2 3 4
0 0
0
2
For : 56.31 71.57 90 108.43
= 70.55 (2 1)180
Therefore 2 3 is not a point on root locus
For : 180
2
Hence 2 is on root locus for some value of .
2
Moreov
i
i
P
k
j
P
j K
θ θ θ θ θ
θ
= + − − = + − −
− ≠ +
− +
=
⎛ ⎞
− + ⎜ ⎟
⎜ ⎟
⎝ ⎠
∑
∑
3 4
1 2
2
(1.22)
2
er, 0.33
(2.12) (1.22)
L L
K
L L
×
= = =
×

14
Summary
Given the poles and zeros of the open loop transfer function
( ) ( ), a point in the -plane is on the root locus for a
particular value of gain , if the angles of the zeros minus the
angles of the po
KG s H s s
K
0
les add up to (2 1)180 .
Furthermore, the gain at that point can be found by dividing
the product of the lengths of the poles by the product of the
lengths of the zeros.
k
K
+

15
Sketching the Root Locus:
Basic Five Rules
1. Number of Branches: The number of branches of the root locus
equals the number of closed loop poles (since each pole should
move as the gain varies).
2. Symmetricity: A root locus is always symmetric about the real
axis, since complex poles must always appear in conjugate pairs.
3. Real-axis segments: On the real axis, for K > 0, the root locus
exists to the left of an odd number of real-axis finite open-loop
poles and zeros.

16
Sketching the Root Locus:
Basic Five Rules
4. Starting and ending points: The root locus begins at the finite
and infinite poles of G(s)H(s) and ends at the finite and infinite
zeros of G(s)H(s)
5. Behavior at Infinity: The root locus approaches straight lines
as asymptotes as the locus approaches infinity
a
a
The equation of asymptotes is given by the real-axis intercept
and angle as follows:
. .
(2 1)
. .
a
a
finite poles finite zeros
No of finite poles No of finite zeros
k
No of finite poles No of finite zeros
where k
σ
θ
σ
θ
−
=
−
+ Π
=
−
∑ ∑
0, 1, 2, 3,.....
.
and the angle is given in radians wrt positive extension of real axis
= ± ± ±
For additional rules, refer to:
N. S. Nise: Control Systems
Engineering, 4th Ed., Wiley, 2004.

17
Example

18
Control Tuning via Gain
Adjustment using Root Locus
Sample root locus:
A. Possible design point via
gain adjustment
B. Desired design point that
cannot be met via simple
gain adjustment (needs
dynamic compensators)

PID Control Design
Dr. Radhakant Padhi
Asst. Professor

20
Introduction
z The PID controller
involves three
components:
• Proportional feedback
• Integral feedback
• Derivative feedback
z By tuning the three
components (gains), a
suitable control action is
generated that leads to
desirable closed loop
response of the output.
Ref : en.wikipedia.org/wiki/PID_controller
( )
u t
( )
U s
( )
E s
Ref: N. S. Nise: Control Systems Engineering, 4th
Ed., Wiley, 2004.

21
Philosophy of PID Design
z The proportional component determines the reaction to the
current value of the output error. It serves as a “all pass”
block.
z The integral component determines the reaction based on
the integral (sum) of recent errors. In a way, it accounts
for the history of the error and serves as a “low pass” block.
z The derivative component determines the reaction based
on the rate of change of the error. In a way, it accounts for
the future value of the error and serves as a “high pass”
block.

22
PID Controller
The final form of the PID algorithm is:
( )= ( ) ( ) ( )
( )
/
( )
The tuning parameters are:
Proportional gain,
Integral gain, /
Derivative gain,
t
p i d
o
p i d
p
i p i
d p d
d
u t K e t K e d K e t
dt
U s
K K s K s
E s
K
K K T
K K T
τ τ
+ +
= + +
=
=
∫

23
Effect of Proportional Term
z Proportional Term:
• If the gain Kp is low, then control action may be too
small when responding to system disturbances. Hence,
it need not lead to desirable performance.
• If the proportional gain Kp is too high, the system can
become unstable. It may also lead to noise amplification
( )
: Proportional term of output
: Proportional gain (a tuning parameter)
out p
out
p
P K e t
P
K
=

24
Effect of Integral Term
Integral term :
0
( )
: Integral term of output
: Integral gain, a tuning parameter
The integral term (when added to the proportional term)
accelerates the movement of the process towards setpoint
and e
t
out i
out
i
I K e d
I
K
τ τ
= ∫
liminates the residual steady-state error that occurs
with a proportional only controller.

25
2 2
i
It can be seen that adding an integral term to a pure proportional
term increases the gain bya factor of
1 1
1+ 1 1, for all .
and simultaneously increases the phase-lag since
1
1+
i
j T T
j T
ω
ω ω
ω
= + >
∠
Destabilizing effects of the Integral term :
1
i
1
tan 0 for all .
Because of this, both the gain margin (GM) and phase margin (PM)
are reduced, and the closed-loopsystem becomes more oscillatory
and potentially unstable.
i
T
ω
ω
− ⎛ ⎞
⎛ ⎞ −
= <
⎜ ⎟
⎜ ⎟
⎝ ⎠ ⎝ ⎠
Ref : Li, Y. , Ang, K.H. and Chong, G.C.Y. (2006)
PID Control System Analysis and Design.
IEEE Control Systems Magazine 26(1):pp. 32-41.

26
• Integrator Windup:
If the actuator that realizes control action has
saturated and if it is neglected, this causes low
frequency oscillations and leads to instability.
Remedies:
Automatic Resetting, Explicit Anti-windup.

27
Effect of Derivative Term
Derivative term :
( )
: Derivative gain (a tuning parameter)
The derivative term speeds up the transient behaviour. In general, it has
negligible effect on the steady state performance (for step inputs,
out d
d
d
D K e t
dt
K
=
•
the
effect on steady state response is zero).
Differentiation of a signal amplifies noise. Hence, this term in
the controller is highly sensitive to noise in the error term! Because
of this, the
•
derivative compensation should be used with care.

28
Effect of Derivative Term
( ) [ ]
1
2
Adding a derivative term to pure proportional term reduces the
phase lag by
1 tan 0, / 2 for all
1
which tends to increase the Phase Margin.
In the meantime, however, the gain increases by a factor of
1 1
d
d
d
T
j T
j T T
ω
ω π ω
ω ω
− ⎛ ⎞
∠ + = ∈
⎜ ⎟
⎝ ⎠
+ = + 2
1, forall .
which decreases the Gain Margin.
Hence the overall stability may be improved or degraded.
: Low pass filter, Set-point filter, Pre-filter etc.
d ω
>
Additional requirement

29
Effect of Increasing Gains on
Output Performance
Parameter Rise time Overshoot Settling
time
Error at
equilibrium
Kp Decreases Increases Small
change
Decreases
Ki Decreases Increases Increases Eliminated
Kd Indefinite
(can either
decrease or
increase)
Decreases Decreases No Effect

30
Tuning of PID Design
Offline method. Good for
first-order processes
Results in good tuning in
general
Cohen-Coon
Method Advantages Disadvantages
Manual Tuning No math required. Largely
trial-and-error approach
(based on general
observations)
Requires experienced
personnel
Ziegler–Nichols Proven online method. Very aggressive tuning,
May upset some inherent
advantages of the process
Software Tools Online or offline methods. Some cost and training of
personnel is involved

31
Ziegler–Nichols Method for Gain
Tuning
The Ki and Kd gains are first set to zero. The P gain is increased
until it reaches the critical gain, Kc, at which the output of the loop
starts to oscillate. Next, Kc and the oscillation period Pc are used to
set the gains as per the following rule.
Control Type Kp Ki Kd
P 0.50Kc - -
PI 0.45Kc 1.2Kp / Pc -
PID 0.60Kc 2Kp / Pc KpPc / 8
Note: The constants used may vary depending on the application.

32
Limitations of PID control
z It is a SISO design approach and hence can
effectively handle only such system
z System should behave in a fairly linear manner.
Hence, it is valid in close proximity of an operating
point (about which the linearized system is valid)
z Does not take into account the limitations of the
actuators
Techniques to overcome:
• Gain scheduling, Cascading controllers, Filters in loop etc.

33

34

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