SlideShare a Scribd company logo
Digital implementation of Analog Controllers
Direct control design - Analytical Method
Dr. Amin Danial
References
❑ Katsuhiko Ogata Discrete-Time Control
Systems 2nd edition,1995
❑ M. Sami Fadali, Antonio Visioli - Digital
Control Engineering, Analysis and Design-
Second Edition_Academic Press (2019)
❑ Gene F. Franklin, J. David Powell, Michael L.
Workman - Digital Control of Dynamic
Systems-Prentice Hall (1998)
❑ A. V. OPPENHEIM, A. S. WILLSKY and S. H.
NAWAB , Signals & Systems, PRENTICE HALL,
1996.
Digital implementation of analog controller design
β€’This lecture introduces an indirect approach to digital
controller design.
β€’The approach is based on designing an analog controller
for the analog subsystem and then obtaining an
equivalent digital controller and using it to digitally
implement the desired control.
β€’The digital controller can be obtained using a number of
methods that are well known in the field of signal
processing, where they are used in the design of digital
filters.
β€’In fact, a controller can be viewed as a filter that
attenuates some dynamics and enhance others so as to
obtain the desired time response.
Procedure:
1. Design a controller 𝐢(𝑠) for the analog subsystem to
meet the desired design specifications.
2. Map the analog controller to a digital controller 𝐢(𝑧)
using a suitable transformation.
3. Tune the gain of the transfer function meet the design
specifications.
4. Check the sampled time response of the digital control
system and repeat steps 1 to 3, if necessary, until the
design specifications are met.
Digital implementation of analog controller design
β€’ The transformation from an analog to a digital
filterβ€”must satisfy the following requirements:
1. A stable analog filter (poles in the left half plane
(LHP)) must transform to a stable digital filter.
2. The frequency response of the digital filter
must closely resemble the frequency response
of the analog filter in the frequency range 0 βˆ’
πœ”π‘ 
2
where πœ”π‘  is the sampling frequency.
β€’ Most filter transformations satisfy these two
requirements to varying degrees.
β€’ However, this is not true of all analog-to-digital
transformations
Digital implementation of analog controller design
β€’ The forward differencing approximation of the derivative is:
ሢ
𝑦(π‘˜π‘‡) β‰…
𝑦 π‘˜ + 1 𝑇 βˆ’ 𝑦 π‘˜π‘‡
𝑇
β€’ In the same way
ሷ
𝑦 π‘˜π‘‡ β‰…
ሢ
𝑦 π‘˜ + 1 𝑇 βˆ’ ሢ
𝑦(π‘˜π‘‡)
𝑇
β‰…
1
𝑇
𝑦 π‘˜ + 2 𝑇 βˆ’ 𝑦 π‘˜ + 1 𝑇
𝑇
βˆ’
𝑦 π‘˜ + 1 𝑇 βˆ’ 𝑦 π‘˜π‘‡
𝑇
β‰…
1
𝑇2
𝑦 π‘˜ + 2 𝑇 βˆ’ 2𝑦 π‘˜ + 1 𝑇 + 𝑦 π‘˜π‘‡
β€’ This yields the mapping of 𝑠 to 𝑧 as follows:
π‘ π‘Œ 𝑠 β†’
𝑧 βˆ’ 1
𝑇
π‘Œ(𝑧)
β€’ Therefore, the direct transformation of an s-transfer function to a z-transfer
function is possible using the substitution
𝑠 β†’
𝑧 βˆ’ 1
𝑇
Digital implementation of analog controller design
Differencing methods - Forward differencing
β€’ Ex1: Apply the forward difference approximation of the
derivative to the second-order analog filter:
and examine the stability of the resulting digital
filter for a stable analog filter.
Digital implementation of analog controller design
Differencing methods - Forward differencing
β€’ EX1(Cont.): Solution: (Note 𝑦(π‘˜π‘‡) ≑ 𝑦(π‘˜))
β€’ The differential equation from the given transfer function is:
β€’ Then
β€’ By multiply both sides by 𝑇2and rearrange the equation
β€’ Equivalently, we obtain the transfer function of the filter using the
simpler transformation
Digital implementation of analog controller design
Differencing methods - Forward differencing
β€’ EX1(Cont.): Solution:
β€’ For a stable analog filter, we have 𝜁 > 0 and πœ”π‘› > 0
(positive denominator coefficients are sufficient for a
second-order polynomial)
β€’ From Jury test, the instability condition π‘Žπ‘› > π‘Ž0 :
β€’ If the sampling period of 0.2 s and an undamped natural
frequency of 10 rad/s yield unstable filters for any
underdamped analog filter.
Digital implementation of analog controller design
Differencing methods - Forward differencing
β€’ The backward differencing approximation of the derivative is:
(Note 𝑦(π‘˜π‘‡) ≑ 𝑦(π‘˜))
β€’ Similarly
β€’ This yields the substitution
Digital implementation of analog controller design
Differencing methods - Backward differencing
β€’ Ex2: Apply the backward difference approximation of the
derivative to the second-order analog filter.
and examine the stability of the resulting digital filter for a stable
analog filter.
Solution:
Digital implementation of analog controller design
Differencing methods - Backward differencing
β€’ Ex2: (cont.)
β€’ The stability conditions for the digital filter (Jury test) are
β€’ The conditions are all satisfied for 𝜁 > 0 and πœ”π‘› > 0β€”that is, for
all stable analog filters.
Digital implementation of analog controller design
Differencing methods - Backward differencing
π‘Žπ‘› < π‘Ž0 β†’
𝑃 1 > 0 β†’
𝑃 βˆ’1 > 0 β†’
β€’ In pole-zero matching, a discrete approximation is obtained from an
analog filter by mapping both poles and zeros using 𝑃𝑠 = 𝑒𝑃𝑠𝑇
or
𝑃𝑧 = 𝑒𝑃𝑧𝑇
, where 𝑃𝑠 or 𝑃𝑧 is a pole or a zero in the z-domain and the
s-domain, respectively.
β€’ If the analog filter has 𝑛 poles and π‘š zeros, then we say that the
filter has 𝑛 βˆ’ π‘š zeros at infinity.
β€’ For 𝑛 βˆ’ π‘š zeros at infinity, we add 𝑛 βˆ’ π‘š βˆ’ 1 digital filter zeros at
βˆ’ 1. Leading to the computation of the output requires values of the
input at past sampling points
β€’ If the zeros are not added, it can be shown that the resulting system
will include a time delay.
β€’ Finally, we adjust the gain of the digital filter so that it is equal to that
of the analog filter at a critical frequency dependent on the filter. For
a low-pass filter, 𝛼 is selected so that the gains are equal at DC; for a
bandpass filter, they are set equal at the center of the pass band.
Digital implementation of analog controller design
Pole-zero matching
β€’ For analog filter:
πΊπ‘Ž 𝑠 = 𝐾
ς𝑖=1
π‘š
(𝑠 βˆ’ π‘Žπ‘–)
ς𝑗=1
𝑛
(𝑠 βˆ’ 𝑏𝑗)
β€’ We get the corresponding digital filter as follows:
𝐺 𝑧 = 𝛼𝐾
𝑧 + 1 π‘›βˆ’π‘šβˆ’1 ς𝑖=1
π‘š
(𝑧 βˆ’ π‘’π‘Žπ‘–π‘‡)
ς𝑗=1
𝑛
(𝑧 βˆ’ 𝑒𝑏𝑗𝑇
)
β€’ Where Ξ± is a constant selected for equal filter gains at a critical
frequency. For example, for a low-pass filter, Ξ± is selected to match
the DC gains using 𝐺 1 = πΊπ‘Ž(0).
β€’ For a high-pass filter, it is selected to match the high-frequency
gains using 𝐺 βˆ’1 = πΊπ‘Ž ∞ (Setting 𝑧 = 𝑒 π‘—πœ”π‘‡ = βˆ’1 (i.e., πœ”π‘‡ =
πœ‹) is equivalent to selecting the folding frequency πœ”π‘ /2, which is
the highest frequency allowable without aliasing)
Digital implementation of analog controller design
Pole-zero matching
β€’ Ex3: Find a pole-zero matched digital filter
approximation for the analog filter.
If the damping ratio is equal to 0.5 and the undamped
natural frequency is 5 rad/s, determine the transfer
function of the digital filter for a sampling period of 0.1 s.
Digital implementation of analog controller design
Pole-zero matching
Ex3: (cont.) Solution
β€’ The analog filter has two zeros at infinity and complex conjugate
poles at 𝑠 = βˆ’πœπœ”π‘› Β± π‘—πœ”π‘‘.
β€’ using the pole-zero matching transformation we get
𝐺 𝑧 = 𝛼
𝑧 + 1
𝑧 βˆ’ π‘’βˆ’πœπœ”π‘›π‘‡π‘’π‘—πœ”π‘‘π‘‡ ( 𝑧 βˆ’ π‘’βˆ’πœπœ”π‘›π‘‡π‘’βˆ’π‘—πœ”π‘‘π‘‡
𝐺 𝑧 = 𝛼
𝑧 + 1
𝑧2 βˆ’ 2π‘’βˆ’πœπœ”π‘›π‘‡ cos πœ”π‘‘π‘‡ + π‘’βˆ’2πœπœ”π‘›π‘‡
For 𝜁 = 0.5, πœ”π‘› = 5, π‘Žπ‘›π‘‘ 𝑇 = 0.1. then πœ”π‘‘ = πœ”π‘› 1 βˆ’ 𝜁2, the gain 𝛼
is determined from 𝐺 1 = πΊπ‘Ž 0
Therefore,
𝐺 𝑧 =
0.0963(𝑧 + 1)
𝑧2 βˆ’ 1.414𝑧 + 0.6065
Digital implementation of analog controller design
Pole-zero matching
β€’ Using the relation :
𝑧 = 𝑒𝑠𝑇
=
𝑒
𝑠𝑇
2
π‘’βˆ’
𝑠𝑇
2
β‰…
1 +
𝑠𝑇
2
1 βˆ’
𝑠𝑇
2
Then
𝑠 =
2
𝑇
𝑧 βˆ’ 1
𝑧 + 1
β€’ The bilinear transformation maps points in the
LHP to points inside the unit circle and thus
guarantees the stability of a digital filter for a
stable analog filter.
Digital implementation of analog controller design
Bilinear transformation
β€’ Ex4: Design a digital filter by applying the bilinear transformation
to the analog filter
πΆπ‘Ž 𝑠 =
1
0.1𝑠 + 1
with 𝑇 = 0.1 s.
Solution:
Digital implementation of analog controller design
Bilinear transformation
β€’ Ex5: design a PI controller for the following
system
πΊπ‘Ž 𝑠 =
1
𝑠 + 2
to meet the following specs:
1. Damping ratio is 0.5
2. Natural undamped frequency is 5rad/sec
Then implement it digitally using Backward
difference 𝑇𝑠 = 0.01𝑠.
And then draw the whole system block diagram.
Digital implementation of analog controller design
β€’ Ex5 (cont.)
β€’ It is required that 𝜁 = 0.5 and πœ”π‘› = 5
β€’ The controller TF is πΆπ‘Ž 𝑠 =
𝐾𝑝𝑠+𝐾𝑖
𝑠
β€’ The characteristic equation of the closed loop system
is 1 + πΆπ‘Ž 𝑠 πΊπ‘Ž 𝑠 = 0
1 +
𝐾𝑝𝑠 + 𝐾𝑖
𝑠
.
1
𝑠 + 2
= 0
𝑠2
+ 𝐾𝑝 + 2 𝑠 + 𝐾𝑖 = 0
The required characteristic equation is:
𝑠2
+ 2πœπœ”π‘›s + πœ”π‘›
2
= 0
Therefore
𝑠2
+ 𝐾𝑝 + 2 𝑠 + 𝐾𝑖 = 𝑠2
+ 2πœπœ”π‘›s + πœ”π‘›
2
Digital implementation of analog controller design
β€’ Ex5 (cont.)
β€’ By comparing the coefficients, we get:
𝐾𝑝 + 2 = 2 βˆ— 0.5 βˆ— 5
𝐾𝑝 = 3
𝐾𝑖 = πœ”π‘›
2
= 25
Therefore, the PI controller is
πΆπ‘Ž 𝑠 =
3𝑠 + 25
𝑠
By using the backward difference method
𝑠 =
𝑧 βˆ’ 1
𝑧𝑇𝑠
Digital implementation of analog controller design
β€’ Ex5 (cont.)
𝐢 𝑧 = α‰š
πΆπ‘Ž 𝑠
𝑠=
π‘§βˆ’1
𝑧𝑇𝑠
=
3
𝑧 βˆ’ 1
𝑧𝑇𝑠
+ 25
𝑧 βˆ’ 1
𝑧𝑇𝑠
𝐢 𝑧 =
3 + 25𝑇𝑠 𝑧 βˆ’ 3
𝑧 βˆ’ 1
=
3.25𝑧 βˆ’ 3
𝑧 βˆ’ 1
Digital implementation of analog controller design
β€’ Ex5 (cont.)
Digital implementation of analog controller design
Block Diagram
O/P
I/P
β€’ Ex5 (cont.)
Digital implementation of analog controller design
Analog Controller
Digital Controller
β€’ For the following system:
Where
𝐺 𝑧 = π‘π‘‘π‘Ÿπ‘Žπ‘›π‘ 
1 βˆ’ π‘’βˆ’π‘ π‘‡
𝑠
𝐺𝑝 𝑠
Direct control design - Analytical Method
β€’ In this approach, it is required to find the desired
closed loop pulse transfer function 𝐹 𝑧 (This
approach to design is known as synthesis)
β€’ Then
𝐢 𝑧
𝑅 𝑧
=
𝐺𝐷 𝑧 𝐺 𝑧
1 + 𝐺𝐷 𝑧 𝐺 𝑧
= 𝐹(𝑧)
Consequently, the controller is found as follows:
𝐺𝐷 𝑧 =
𝐹 𝑧
𝐺(𝑧) 1 βˆ’ 𝐹 𝑧
Direct control design - Analytical Method
β€’ The designed system must be physically
realizable. The conditions for physical realizability
are as follows:
1. The order of numerator of 𝐺𝐷 𝑧 must be equal or
lower than the order of the denominator. Otherwise,
the controller will be noncausal, i.e. it needs future
input to produce the current output.
2. If the plant has a delay π‘’βˆ’π‘™π‘  then the designed closed
loop system must have at least the same delay.
Otherwise, the closed loop system would have to
respond before an input was give, which is physically
impossible.
Direct control design - Analytical Method
3. We must avoid of canceling any unstable pole of the plant be a
zero of the controller because any error in the cancellation will
cause instability.
If 𝐺(𝑧) has a pole at 𝛼 then:
𝐺 𝑧 =
𝐺1 𝑧
𝑧 βˆ’ 𝛼
Where 𝐺1 𝑧 has no term that cancels 𝑧 βˆ’ 𝛼.
Consequently,
𝐹 𝑧 =
𝐺𝐷 𝑧
𝐺1 𝑧
𝑧 βˆ’ 𝛼
1 + 𝐺𝐷(𝑧)
𝐺1 𝑧
𝑧 βˆ’ 𝛼
1 βˆ’ 𝐹 𝑧 =
1
1 + 𝐺𝐷 𝑧
𝐺1 𝑧
𝑧 βˆ’ 𝛼
=
𝑧 βˆ’ 𝛼
𝑧 βˆ’ 𝛼 + 𝐺𝐷 𝑧 𝐺1 𝑧
Direct control design - Analytical Method
Therefore, 𝛼 is zero of 1 βˆ’ 𝐹 𝑧 , hence:
𝐹 𝛼 = 1
4. The poles of the controller 𝐺𝐷(𝑧) do not cancel zeros of
𝐺 𝑧 which lies outside the unit circle.
Therefore, if there is a zero (𝑧 βˆ’ 𝛽)of 𝐺(𝑧) which is outside
the unit circle. Then, we can write 𝐺 𝑧 as follows:
𝐺 𝑧 = 𝑧 βˆ’ 𝛽 𝐺2(𝑧)
Consequently, in case of (𝑧 βˆ’ 𝛽) is not cancelled:
𝐹 𝑧 =
𝐺𝐷 𝑧 𝑧 βˆ’ 𝛽 𝐺2(𝑧)
1 + 𝐺𝐷(𝑧) 𝑧 βˆ’ 𝛽 𝐺2(𝑧)
Hence,
𝐹 𝛽 = 0
Direct control design - Analytical Method
5. An additional condition can be imposed to
address steady-state accuracy
requirements.
In particular, if zero steady-state error due
to a step input is required
lim
π‘˜β†’βˆž
𝑐 π‘˜π‘‡ = lim
𝑧→1
𝑧 βˆ’ 1 𝐹 𝑧 .
𝑧
𝑧 βˆ’ 1
= 1
Therefore,
𝐹 1 = 1
Direct control design - Analytical Method
β€’ Steps of design
1.Select the desired settling time Ts and the desired
maximum overshoot.
2.Select a suitable continuous-time closed-loop first-
order or second-order closed-loop system with unit
gain.
3.Obtain by converting the s-plane pole location to the
z-plane pole location using pole-zero matching.
4.Verify that 𝐹(𝑧) meets the conditions for causality,
stability, and steady-state error. If not, modify 𝐹(𝑧)
until the conditions are met.
Direct control design - Analytical Method
β€’ EX6: Design a digital controller for a DC motor speed control
system where the (type 0) analog plant has the transfer function
𝐺𝑝 =
1
(𝑠 + 1)(𝑠 + 10)
To obtain zero steady-state error due to a unit step, πœ”π‘› =
1.15 π‘Ÿπ‘Žπ‘‘/𝑠, and 𝜁 = 0.88. The sampling period is chosen as 𝑇 =
0.02 s
β€’ Solution
Since in digital control system a ZOH is always inserted between the
digital controller and the system to be controlled, hence
𝐺 𝑧 = π‘π‘‘π‘Ÿπ‘Žπ‘›π‘ 
1 βˆ’ π‘’βˆ’π‘ π‘‡
𝑠
𝐺𝑝 𝑠 = (1 βˆ’ π‘§βˆ’1)π‘π‘‘π‘Ÿπ‘Žπ‘›π‘ 
𝐺𝑝 𝑠
𝑠
Direct control design - Analytical Method
β€’ EX6 (cont.)
𝐺 𝑧 = 1.86 Γ— 10βˆ’4
𝑧 + 0.9293
(𝑧 βˆ’ 0.8187)(𝑧 βˆ’ 0.9802)
Then, based on the requirements 𝐹 𝑠 is:
𝐹 𝑠 =
πœ”π‘›
2
𝑠2 + 2πœπœ”π‘›π‘  + πœ”π‘›
2 =
1.322
𝑠2 + 2024𝑠 + 1.322
Using zero-pole matching
𝐹 𝑧 = 0.25921 Γ— 10βˆ’3
𝑧 + 1
𝑧2 βˆ’ 1.95981𝑧 + 0.96033
Direct control design - Analytical Method
EX6(cont.):
Then from
𝐺𝐷 𝑧 =
𝐹 𝑧
𝐺(𝑧) 1 βˆ’ 𝐹 𝑧
We get
𝐺𝐷 𝑧 =
1.3932(𝑧 βˆ’ 0.8187)(𝑧 βˆ’ 0.9802)(𝑧 + 1)
(𝑧 βˆ’ 1)(𝑧 + 0.9293)(𝑧 βˆ’ 0.9601)
Direct control design - Analytical Method
Thanks

More Related Content

Similar to Lecture_8-Digital implementation of analog controller design.pdf

PID-Control_automation_Engineering_chapter6.ppt
PID-Control_automation_Engineering_chapter6.pptPID-Control_automation_Engineering_chapter6.ppt
PID-Control_automation_Engineering_chapter6.ppt
mohamed abd elrazek
Β 
Real Time Implementation of Active Noise Control
Real Time Implementation of Active Noise ControlReal Time Implementation of Active Noise Control
Real Time Implementation of Active Noise Control
Chittaranjan Baliarsingh
Β 
DSP_2018_FOEHU - Lec 05 - Digital Filters
DSP_2018_FOEHU - Lec 05 - Digital FiltersDSP_2018_FOEHU - Lec 05 - Digital Filters
DSP_2018_FOEHU - Lec 05 - Digital Filters
Amr E. Mohamed
Β 
control_5.pptx
control_5.pptxcontrol_5.pptx
control_5.pptx
ewnetukassa2
Β 
Automatic agriculture assistance
Automatic agriculture assistanceAutomatic agriculture assistance
Automatic agriculture assistance
Surajkumar Lal
Β 
Advanced Nonlinear PID-Based Antagonistic Control for Pneumatic Muscle Actuators
Advanced Nonlinear PID-Based Antagonistic Control for Pneumatic Muscle ActuatorsAdvanced Nonlinear PID-Based Antagonistic Control for Pneumatic Muscle Actuators
Advanced Nonlinear PID-Based Antagonistic Control for Pneumatic Muscle Actuators
manikuty123
Β 
Development of Digital Controller for DC-DC Buck Converter
Development of Digital Controller for DC-DC Buck ConverterDevelopment of Digital Controller for DC-DC Buck Converter
Development of Digital Controller for DC-DC Buck Converter
IJPEDS-IAES
Β 
adaptive_ecg_cdr_edittedforpublic.pptx
adaptive_ecg_cdr_edittedforpublic.pptxadaptive_ecg_cdr_edittedforpublic.pptx
adaptive_ecg_cdr_edittedforpublic.pptx
ssuser6f1a8e1
Β 
Filter design techniques ch7 iir
Filter design techniques ch7 iirFilter design techniques ch7 iir
Filter design techniques ch7 iir
Falah Mohammed
Β 
3271829.ppt
3271829.ppt3271829.ppt
3271829.ppt
AhmedHeskol2
Β 
Applying Smoothing Techniques to Passive Target Tracking.pptx
Applying Smoothing Techniques to Passive Target Tracking.pptxApplying Smoothing Techniques to Passive Target Tracking.pptx
Applying Smoothing Techniques to Passive Target Tracking.pptx
ismailshaik2023
Β 
Pid controller
Pid controllerPid controller
Pid controller
Dr. Chetan Bhatt
Β 
Digital counter
Digital counter Digital counter
Digital counter
Bhaskar Kumar Jha
Β 
Control Signal Flow Graphs lecture notes
Control Signal Flow Graphs  lecture notesControl Signal Flow Graphs  lecture notes
Control Signal Flow Graphs lecture notes
abbas miry
Β 
SFG.pptx
SFG.pptxSFG.pptx
SFG.pptx
abbas miry
Β 
iir_filter_design.pptx
iir_filter_design.pptxiir_filter_design.pptx
iir_filter_design.pptx
AswiniSamantray2
Β 
Control System Notes for Engineering.pdf
Control System Notes for Engineering.pdfControl System Notes for Engineering.pdf
Control System Notes for Engineering.pdf
saiyadmuslemali60
Β 
DSP_FOEHU - Lec 07 - Digital Filters
DSP_FOEHU - Lec 07 - Digital FiltersDSP_FOEHU - Lec 07 - Digital Filters
DSP_FOEHU - Lec 07 - Digital Filters
Amr E. Mohamed
Β 
Filters.pdf
Filters.pdfFilters.pdf
Filters.pdf
snehasingh75493
Β 
lecture_37.pptx
lecture_37.pptxlecture_37.pptx
lecture_37.pptx
snehasingh75493
Β 

Similar to Lecture_8-Digital implementation of analog controller design.pdf (20)

PID-Control_automation_Engineering_chapter6.ppt
PID-Control_automation_Engineering_chapter6.pptPID-Control_automation_Engineering_chapter6.ppt
PID-Control_automation_Engineering_chapter6.ppt
Β 
Real Time Implementation of Active Noise Control
Real Time Implementation of Active Noise ControlReal Time Implementation of Active Noise Control
Real Time Implementation of Active Noise Control
Β 
DSP_2018_FOEHU - Lec 05 - Digital Filters
DSP_2018_FOEHU - Lec 05 - Digital FiltersDSP_2018_FOEHU - Lec 05 - Digital Filters
DSP_2018_FOEHU - Lec 05 - Digital Filters
Β 
control_5.pptx
control_5.pptxcontrol_5.pptx
control_5.pptx
Β 
Automatic agriculture assistance
Automatic agriculture assistanceAutomatic agriculture assistance
Automatic agriculture assistance
Β 
Advanced Nonlinear PID-Based Antagonistic Control for Pneumatic Muscle Actuators
Advanced Nonlinear PID-Based Antagonistic Control for Pneumatic Muscle ActuatorsAdvanced Nonlinear PID-Based Antagonistic Control for Pneumatic Muscle Actuators
Advanced Nonlinear PID-Based Antagonistic Control for Pneumatic Muscle Actuators
Β 
Development of Digital Controller for DC-DC Buck Converter
Development of Digital Controller for DC-DC Buck ConverterDevelopment of Digital Controller for DC-DC Buck Converter
Development of Digital Controller for DC-DC Buck Converter
Β 
adaptive_ecg_cdr_edittedforpublic.pptx
adaptive_ecg_cdr_edittedforpublic.pptxadaptive_ecg_cdr_edittedforpublic.pptx
adaptive_ecg_cdr_edittedforpublic.pptx
Β 
Filter design techniques ch7 iir
Filter design techniques ch7 iirFilter design techniques ch7 iir
Filter design techniques ch7 iir
Β 
3271829.ppt
3271829.ppt3271829.ppt
3271829.ppt
Β 
Applying Smoothing Techniques to Passive Target Tracking.pptx
Applying Smoothing Techniques to Passive Target Tracking.pptxApplying Smoothing Techniques to Passive Target Tracking.pptx
Applying Smoothing Techniques to Passive Target Tracking.pptx
Β 
Pid controller
Pid controllerPid controller
Pid controller
Β 
Digital counter
Digital counter Digital counter
Digital counter
Β 
Control Signal Flow Graphs lecture notes
Control Signal Flow Graphs  lecture notesControl Signal Flow Graphs  lecture notes
Control Signal Flow Graphs lecture notes
Β 
SFG.pptx
SFG.pptxSFG.pptx
SFG.pptx
Β 
iir_filter_design.pptx
iir_filter_design.pptxiir_filter_design.pptx
iir_filter_design.pptx
Β 
Control System Notes for Engineering.pdf
Control System Notes for Engineering.pdfControl System Notes for Engineering.pdf
Control System Notes for Engineering.pdf
Β 
DSP_FOEHU - Lec 07 - Digital Filters
DSP_FOEHU - Lec 07 - Digital FiltersDSP_FOEHU - Lec 07 - Digital Filters
DSP_FOEHU - Lec 07 - Digital Filters
Β 
Filters.pdf
Filters.pdfFilters.pdf
Filters.pdf
Β 
lecture_37.pptx
lecture_37.pptxlecture_37.pptx
lecture_37.pptx
Β 

Recently uploaded

Harnessing WebAssembly for Real-time Stateless Streaming Pipelines
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesHarnessing WebAssembly for Real-time Stateless Streaming Pipelines
Harnessing WebAssembly for Real-time Stateless Streaming Pipelines
Christina Lin
Β 
Understanding Inductive Bias in Machine Learning
Understanding Inductive Bias in Machine LearningUnderstanding Inductive Bias in Machine Learning
Understanding Inductive Bias in Machine Learning
SUTEJAS
Β 
Comparative analysis between traditional aquaponics and reconstructed aquapon...
Comparative analysis between traditional aquaponics and reconstructed aquapon...Comparative analysis between traditional aquaponics and reconstructed aquapon...
Comparative analysis between traditional aquaponics and reconstructed aquapon...
bijceesjournal
Β 
Question paper of renewable energy sources
Question paper of renewable energy sourcesQuestion paper of renewable energy sources
Question paper of renewable energy sources
mahammadsalmanmech
Β 
Advanced control scheme of doubly fed induction generator for wind turbine us...
Advanced control scheme of doubly fed induction generator for wind turbine us...Advanced control scheme of doubly fed induction generator for wind turbine us...
Advanced control scheme of doubly fed induction generator for wind turbine us...
IJECEIAES
Β 
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
IJECEIAES
Β 
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressions
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsKuberTENes Birthday Bash Guadalajara - K8sGPT first impressions
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressions
Victor Morales
Β 
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELDEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
gerogepatton
Β 
132/33KV substation case study Presentation
132/33KV substation case study Presentation132/33KV substation case study Presentation
132/33KV substation case study Presentation
kandramariana6
Β 
Casting-Defect-inSlab continuous casting.pdf
Casting-Defect-inSlab continuous casting.pdfCasting-Defect-inSlab continuous casting.pdf
Casting-Defect-inSlab continuous casting.pdf
zubairahmad848137
Β 
Modelagem de um CSTR com reação endotermica.pdf
Modelagem de um CSTR com reação endotermica.pdfModelagem de um CSTR com reação endotermica.pdf
Modelagem de um CSTR com reação endotermica.pdf
camseq
Β 
Engineering Drawings Lecture Detail Drawings 2014.pdf
Engineering Drawings Lecture Detail Drawings 2014.pdfEngineering Drawings Lecture Detail Drawings 2014.pdf
Engineering Drawings Lecture Detail Drawings 2014.pdf
abbyasa1014
Β 
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSA SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
IJNSA Journal
Β 
Manufacturing Process of molasses based distillery ppt.pptx
Manufacturing Process of molasses based distillery ppt.pptxManufacturing Process of molasses based distillery ppt.pptx
Manufacturing Process of molasses based distillery ppt.pptx
Madan Karki
Β 
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
Sinan KOZAK
Β 
Literature Review Basics and Understanding Reference Management.pptx
Literature Review Basics and Understanding Reference Management.pptxLiterature Review Basics and Understanding Reference Management.pptx
Literature Review Basics and Understanding Reference Management.pptx
Dr Ramhari Poudyal
Β 
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdfBPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
MIGUELANGEL966976
Β 
Iron and Steel Technology Roadmap - Towards more sustainable steelmaking.pdf
Iron and Steel Technology Roadmap - Towards more sustainable steelmaking.pdfIron and Steel Technology Roadmap - Towards more sustainable steelmaking.pdf
Iron and Steel Technology Roadmap - Towards more sustainable steelmaking.pdf
RadiNasr
Β 
Unit-III-ELECTROCHEMICAL STORAGE DEVICES.ppt
Unit-III-ELECTROCHEMICAL STORAGE DEVICES.pptUnit-III-ELECTROCHEMICAL STORAGE DEVICES.ppt
Unit-III-ELECTROCHEMICAL STORAGE DEVICES.ppt
KrishnaveniKrishnara1
Β 
Embedded machine learning-based road conditions and driving behavior monitoring
Embedded machine learning-based road conditions and driving behavior monitoringEmbedded machine learning-based road conditions and driving behavior monitoring
Embedded machine learning-based road conditions and driving behavior monitoring
IJECEIAES
Β 

Recently uploaded (20)

Harnessing WebAssembly for Real-time Stateless Streaming Pipelines
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesHarnessing WebAssembly for Real-time Stateless Streaming Pipelines
Harnessing WebAssembly for Real-time Stateless Streaming Pipelines
Β 
Understanding Inductive Bias in Machine Learning
Understanding Inductive Bias in Machine LearningUnderstanding Inductive Bias in Machine Learning
Understanding Inductive Bias in Machine Learning
Β 
Comparative analysis between traditional aquaponics and reconstructed aquapon...
Comparative analysis between traditional aquaponics and reconstructed aquapon...Comparative analysis between traditional aquaponics and reconstructed aquapon...
Comparative analysis between traditional aquaponics and reconstructed aquapon...
Β 
Question paper of renewable energy sources
Question paper of renewable energy sourcesQuestion paper of renewable energy sources
Question paper of renewable energy sources
Β 
Advanced control scheme of doubly fed induction generator for wind turbine us...
Advanced control scheme of doubly fed induction generator for wind turbine us...Advanced control scheme of doubly fed induction generator for wind turbine us...
Advanced control scheme of doubly fed induction generator for wind turbine us...
Β 
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Β 
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressions
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsKuberTENes Birthday Bash Guadalajara - K8sGPT first impressions
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressions
Β 
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELDEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
Β 
132/33KV substation case study Presentation
132/33KV substation case study Presentation132/33KV substation case study Presentation
132/33KV substation case study Presentation
Β 
Casting-Defect-inSlab continuous casting.pdf
Casting-Defect-inSlab continuous casting.pdfCasting-Defect-inSlab continuous casting.pdf
Casting-Defect-inSlab continuous casting.pdf
Β 
Modelagem de um CSTR com reação endotermica.pdf
Modelagem de um CSTR com reação endotermica.pdfModelagem de um CSTR com reação endotermica.pdf
Modelagem de um CSTR com reação endotermica.pdf
Β 
Engineering Drawings Lecture Detail Drawings 2014.pdf
Engineering Drawings Lecture Detail Drawings 2014.pdfEngineering Drawings Lecture Detail Drawings 2014.pdf
Engineering Drawings Lecture Detail Drawings 2014.pdf
Β 
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSA SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
Β 
Manufacturing Process of molasses based distillery ppt.pptx
Manufacturing Process of molasses based distillery ppt.pptxManufacturing Process of molasses based distillery ppt.pptx
Manufacturing Process of molasses based distillery ppt.pptx
Β 
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
Β 
Literature Review Basics and Understanding Reference Management.pptx
Literature Review Basics and Understanding Reference Management.pptxLiterature Review Basics and Understanding Reference Management.pptx
Literature Review Basics and Understanding Reference Management.pptx
Β 
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdfBPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
Β 
Iron and Steel Technology Roadmap - Towards more sustainable steelmaking.pdf
Iron and Steel Technology Roadmap - Towards more sustainable steelmaking.pdfIron and Steel Technology Roadmap - Towards more sustainable steelmaking.pdf
Iron and Steel Technology Roadmap - Towards more sustainable steelmaking.pdf
Β 
Unit-III-ELECTROCHEMICAL STORAGE DEVICES.ppt
Unit-III-ELECTROCHEMICAL STORAGE DEVICES.pptUnit-III-ELECTROCHEMICAL STORAGE DEVICES.ppt
Unit-III-ELECTROCHEMICAL STORAGE DEVICES.ppt
Β 
Embedded machine learning-based road conditions and driving behavior monitoring
Embedded machine learning-based road conditions and driving behavior monitoringEmbedded machine learning-based road conditions and driving behavior monitoring
Embedded machine learning-based road conditions and driving behavior monitoring
Β 

Lecture_8-Digital implementation of analog controller design.pdf

  • 1. Digital implementation of Analog Controllers Direct control design - Analytical Method Dr. Amin Danial
  • 2. References ❑ Katsuhiko Ogata Discrete-Time Control Systems 2nd edition,1995 ❑ M. Sami Fadali, Antonio Visioli - Digital Control Engineering, Analysis and Design- Second Edition_Academic Press (2019) ❑ Gene F. Franklin, J. David Powell, Michael L. Workman - Digital Control of Dynamic Systems-Prentice Hall (1998) ❑ A. V. OPPENHEIM, A. S. WILLSKY and S. H. NAWAB , Signals & Systems, PRENTICE HALL, 1996.
  • 3. Digital implementation of analog controller design β€’This lecture introduces an indirect approach to digital controller design. β€’The approach is based on designing an analog controller for the analog subsystem and then obtaining an equivalent digital controller and using it to digitally implement the desired control. β€’The digital controller can be obtained using a number of methods that are well known in the field of signal processing, where they are used in the design of digital filters. β€’In fact, a controller can be viewed as a filter that attenuates some dynamics and enhance others so as to obtain the desired time response.
  • 4. Procedure: 1. Design a controller 𝐢(𝑠) for the analog subsystem to meet the desired design specifications. 2. Map the analog controller to a digital controller 𝐢(𝑧) using a suitable transformation. 3. Tune the gain of the transfer function meet the design specifications. 4. Check the sampled time response of the digital control system and repeat steps 1 to 3, if necessary, until the design specifications are met. Digital implementation of analog controller design
  • 5. β€’ The transformation from an analog to a digital filterβ€”must satisfy the following requirements: 1. A stable analog filter (poles in the left half plane (LHP)) must transform to a stable digital filter. 2. The frequency response of the digital filter must closely resemble the frequency response of the analog filter in the frequency range 0 βˆ’ πœ”π‘  2 where πœ”π‘  is the sampling frequency. β€’ Most filter transformations satisfy these two requirements to varying degrees. β€’ However, this is not true of all analog-to-digital transformations Digital implementation of analog controller design
  • 6. β€’ The forward differencing approximation of the derivative is: ሢ 𝑦(π‘˜π‘‡) β‰… 𝑦 π‘˜ + 1 𝑇 βˆ’ 𝑦 π‘˜π‘‡ 𝑇 β€’ In the same way ሷ 𝑦 π‘˜π‘‡ β‰… ሢ 𝑦 π‘˜ + 1 𝑇 βˆ’ ሢ 𝑦(π‘˜π‘‡) 𝑇 β‰… 1 𝑇 𝑦 π‘˜ + 2 𝑇 βˆ’ 𝑦 π‘˜ + 1 𝑇 𝑇 βˆ’ 𝑦 π‘˜ + 1 𝑇 βˆ’ 𝑦 π‘˜π‘‡ 𝑇 β‰… 1 𝑇2 𝑦 π‘˜ + 2 𝑇 βˆ’ 2𝑦 π‘˜ + 1 𝑇 + 𝑦 π‘˜π‘‡ β€’ This yields the mapping of 𝑠 to 𝑧 as follows: π‘ π‘Œ 𝑠 β†’ 𝑧 βˆ’ 1 𝑇 π‘Œ(𝑧) β€’ Therefore, the direct transformation of an s-transfer function to a z-transfer function is possible using the substitution 𝑠 β†’ 𝑧 βˆ’ 1 𝑇 Digital implementation of analog controller design Differencing methods - Forward differencing
  • 7. β€’ Ex1: Apply the forward difference approximation of the derivative to the second-order analog filter: and examine the stability of the resulting digital filter for a stable analog filter. Digital implementation of analog controller design Differencing methods - Forward differencing
  • 8. β€’ EX1(Cont.): Solution: (Note 𝑦(π‘˜π‘‡) ≑ 𝑦(π‘˜)) β€’ The differential equation from the given transfer function is: β€’ Then β€’ By multiply both sides by 𝑇2and rearrange the equation β€’ Equivalently, we obtain the transfer function of the filter using the simpler transformation Digital implementation of analog controller design Differencing methods - Forward differencing
  • 9. β€’ EX1(Cont.): Solution: β€’ For a stable analog filter, we have 𝜁 > 0 and πœ”π‘› > 0 (positive denominator coefficients are sufficient for a second-order polynomial) β€’ From Jury test, the instability condition π‘Žπ‘› > π‘Ž0 : β€’ If the sampling period of 0.2 s and an undamped natural frequency of 10 rad/s yield unstable filters for any underdamped analog filter. Digital implementation of analog controller design Differencing methods - Forward differencing
  • 10. β€’ The backward differencing approximation of the derivative is: (Note 𝑦(π‘˜π‘‡) ≑ 𝑦(π‘˜)) β€’ Similarly β€’ This yields the substitution Digital implementation of analog controller design Differencing methods - Backward differencing
  • 11. β€’ Ex2: Apply the backward difference approximation of the derivative to the second-order analog filter. and examine the stability of the resulting digital filter for a stable analog filter. Solution: Digital implementation of analog controller design Differencing methods - Backward differencing
  • 12. β€’ Ex2: (cont.) β€’ The stability conditions for the digital filter (Jury test) are β€’ The conditions are all satisfied for 𝜁 > 0 and πœ”π‘› > 0β€”that is, for all stable analog filters. Digital implementation of analog controller design Differencing methods - Backward differencing π‘Žπ‘› < π‘Ž0 β†’ 𝑃 1 > 0 β†’ 𝑃 βˆ’1 > 0 β†’
  • 13. β€’ In pole-zero matching, a discrete approximation is obtained from an analog filter by mapping both poles and zeros using 𝑃𝑠 = 𝑒𝑃𝑠𝑇 or 𝑃𝑧 = 𝑒𝑃𝑧𝑇 , where 𝑃𝑠 or 𝑃𝑧 is a pole or a zero in the z-domain and the s-domain, respectively. β€’ If the analog filter has 𝑛 poles and π‘š zeros, then we say that the filter has 𝑛 βˆ’ π‘š zeros at infinity. β€’ For 𝑛 βˆ’ π‘š zeros at infinity, we add 𝑛 βˆ’ π‘š βˆ’ 1 digital filter zeros at βˆ’ 1. Leading to the computation of the output requires values of the input at past sampling points β€’ If the zeros are not added, it can be shown that the resulting system will include a time delay. β€’ Finally, we adjust the gain of the digital filter so that it is equal to that of the analog filter at a critical frequency dependent on the filter. For a low-pass filter, 𝛼 is selected so that the gains are equal at DC; for a bandpass filter, they are set equal at the center of the pass band. Digital implementation of analog controller design Pole-zero matching
  • 14. β€’ For analog filter: πΊπ‘Ž 𝑠 = 𝐾 ς𝑖=1 π‘š (𝑠 βˆ’ π‘Žπ‘–) ς𝑗=1 𝑛 (𝑠 βˆ’ 𝑏𝑗) β€’ We get the corresponding digital filter as follows: 𝐺 𝑧 = 𝛼𝐾 𝑧 + 1 π‘›βˆ’π‘šβˆ’1 ς𝑖=1 π‘š (𝑧 βˆ’ π‘’π‘Žπ‘–π‘‡) ς𝑗=1 𝑛 (𝑧 βˆ’ 𝑒𝑏𝑗𝑇 ) β€’ Where Ξ± is a constant selected for equal filter gains at a critical frequency. For example, for a low-pass filter, Ξ± is selected to match the DC gains using 𝐺 1 = πΊπ‘Ž(0). β€’ For a high-pass filter, it is selected to match the high-frequency gains using 𝐺 βˆ’1 = πΊπ‘Ž ∞ (Setting 𝑧 = 𝑒 π‘—πœ”π‘‡ = βˆ’1 (i.e., πœ”π‘‡ = πœ‹) is equivalent to selecting the folding frequency πœ”π‘ /2, which is the highest frequency allowable without aliasing) Digital implementation of analog controller design Pole-zero matching
  • 15. β€’ Ex3: Find a pole-zero matched digital filter approximation for the analog filter. If the damping ratio is equal to 0.5 and the undamped natural frequency is 5 rad/s, determine the transfer function of the digital filter for a sampling period of 0.1 s. Digital implementation of analog controller design Pole-zero matching
  • 16. Ex3: (cont.) Solution β€’ The analog filter has two zeros at infinity and complex conjugate poles at 𝑠 = βˆ’πœπœ”π‘› Β± π‘—πœ”π‘‘. β€’ using the pole-zero matching transformation we get 𝐺 𝑧 = 𝛼 𝑧 + 1 𝑧 βˆ’ π‘’βˆ’πœπœ”π‘›π‘‡π‘’π‘—πœ”π‘‘π‘‡ ( 𝑧 βˆ’ π‘’βˆ’πœπœ”π‘›π‘‡π‘’βˆ’π‘—πœ”π‘‘π‘‡ 𝐺 𝑧 = 𝛼 𝑧 + 1 𝑧2 βˆ’ 2π‘’βˆ’πœπœ”π‘›π‘‡ cos πœ”π‘‘π‘‡ + π‘’βˆ’2πœπœ”π‘›π‘‡ For 𝜁 = 0.5, πœ”π‘› = 5, π‘Žπ‘›π‘‘ 𝑇 = 0.1. then πœ”π‘‘ = πœ”π‘› 1 βˆ’ 𝜁2, the gain 𝛼 is determined from 𝐺 1 = πΊπ‘Ž 0 Therefore, 𝐺 𝑧 = 0.0963(𝑧 + 1) 𝑧2 βˆ’ 1.414𝑧 + 0.6065 Digital implementation of analog controller design Pole-zero matching
  • 17. β€’ Using the relation : 𝑧 = 𝑒𝑠𝑇 = 𝑒 𝑠𝑇 2 π‘’βˆ’ 𝑠𝑇 2 β‰… 1 + 𝑠𝑇 2 1 βˆ’ 𝑠𝑇 2 Then 𝑠 = 2 𝑇 𝑧 βˆ’ 1 𝑧 + 1 β€’ The bilinear transformation maps points in the LHP to points inside the unit circle and thus guarantees the stability of a digital filter for a stable analog filter. Digital implementation of analog controller design Bilinear transformation
  • 18. β€’ Ex4: Design a digital filter by applying the bilinear transformation to the analog filter πΆπ‘Ž 𝑠 = 1 0.1𝑠 + 1 with 𝑇 = 0.1 s. Solution: Digital implementation of analog controller design Bilinear transformation
  • 19. β€’ Ex5: design a PI controller for the following system πΊπ‘Ž 𝑠 = 1 𝑠 + 2 to meet the following specs: 1. Damping ratio is 0.5 2. Natural undamped frequency is 5rad/sec Then implement it digitally using Backward difference 𝑇𝑠 = 0.01𝑠. And then draw the whole system block diagram. Digital implementation of analog controller design
  • 20. β€’ Ex5 (cont.) β€’ It is required that 𝜁 = 0.5 and πœ”π‘› = 5 β€’ The controller TF is πΆπ‘Ž 𝑠 = 𝐾𝑝𝑠+𝐾𝑖 𝑠 β€’ The characteristic equation of the closed loop system is 1 + πΆπ‘Ž 𝑠 πΊπ‘Ž 𝑠 = 0 1 + 𝐾𝑝𝑠 + 𝐾𝑖 𝑠 . 1 𝑠 + 2 = 0 𝑠2 + 𝐾𝑝 + 2 𝑠 + 𝐾𝑖 = 0 The required characteristic equation is: 𝑠2 + 2πœπœ”π‘›s + πœ”π‘› 2 = 0 Therefore 𝑠2 + 𝐾𝑝 + 2 𝑠 + 𝐾𝑖 = 𝑠2 + 2πœπœ”π‘›s + πœ”π‘› 2 Digital implementation of analog controller design
  • 21. β€’ Ex5 (cont.) β€’ By comparing the coefficients, we get: 𝐾𝑝 + 2 = 2 βˆ— 0.5 βˆ— 5 𝐾𝑝 = 3 𝐾𝑖 = πœ”π‘› 2 = 25 Therefore, the PI controller is πΆπ‘Ž 𝑠 = 3𝑠 + 25 𝑠 By using the backward difference method 𝑠 = 𝑧 βˆ’ 1 𝑧𝑇𝑠 Digital implementation of analog controller design
  • 22. β€’ Ex5 (cont.) 𝐢 𝑧 = α‰š πΆπ‘Ž 𝑠 𝑠= π‘§βˆ’1 𝑧𝑇𝑠 = 3 𝑧 βˆ’ 1 𝑧𝑇𝑠 + 25 𝑧 βˆ’ 1 𝑧𝑇𝑠 𝐢 𝑧 = 3 + 25𝑇𝑠 𝑧 βˆ’ 3 𝑧 βˆ’ 1 = 3.25𝑧 βˆ’ 3 𝑧 βˆ’ 1 Digital implementation of analog controller design
  • 23. β€’ Ex5 (cont.) Digital implementation of analog controller design Block Diagram O/P I/P
  • 24. β€’ Ex5 (cont.) Digital implementation of analog controller design Analog Controller Digital Controller
  • 25. β€’ For the following system: Where 𝐺 𝑧 = π‘π‘‘π‘Ÿπ‘Žπ‘›π‘  1 βˆ’ π‘’βˆ’π‘ π‘‡ 𝑠 𝐺𝑝 𝑠 Direct control design - Analytical Method
  • 26. β€’ In this approach, it is required to find the desired closed loop pulse transfer function 𝐹 𝑧 (This approach to design is known as synthesis) β€’ Then 𝐢 𝑧 𝑅 𝑧 = 𝐺𝐷 𝑧 𝐺 𝑧 1 + 𝐺𝐷 𝑧 𝐺 𝑧 = 𝐹(𝑧) Consequently, the controller is found as follows: 𝐺𝐷 𝑧 = 𝐹 𝑧 𝐺(𝑧) 1 βˆ’ 𝐹 𝑧 Direct control design - Analytical Method
  • 27. β€’ The designed system must be physically realizable. The conditions for physical realizability are as follows: 1. The order of numerator of 𝐺𝐷 𝑧 must be equal or lower than the order of the denominator. Otherwise, the controller will be noncausal, i.e. it needs future input to produce the current output. 2. If the plant has a delay π‘’βˆ’π‘™π‘  then the designed closed loop system must have at least the same delay. Otherwise, the closed loop system would have to respond before an input was give, which is physically impossible. Direct control design - Analytical Method
  • 28. 3. We must avoid of canceling any unstable pole of the plant be a zero of the controller because any error in the cancellation will cause instability. If 𝐺(𝑧) has a pole at 𝛼 then: 𝐺 𝑧 = 𝐺1 𝑧 𝑧 βˆ’ 𝛼 Where 𝐺1 𝑧 has no term that cancels 𝑧 βˆ’ 𝛼. Consequently, 𝐹 𝑧 = 𝐺𝐷 𝑧 𝐺1 𝑧 𝑧 βˆ’ 𝛼 1 + 𝐺𝐷(𝑧) 𝐺1 𝑧 𝑧 βˆ’ 𝛼 1 βˆ’ 𝐹 𝑧 = 1 1 + 𝐺𝐷 𝑧 𝐺1 𝑧 𝑧 βˆ’ 𝛼 = 𝑧 βˆ’ 𝛼 𝑧 βˆ’ 𝛼 + 𝐺𝐷 𝑧 𝐺1 𝑧 Direct control design - Analytical Method
  • 29. Therefore, 𝛼 is zero of 1 βˆ’ 𝐹 𝑧 , hence: 𝐹 𝛼 = 1 4. The poles of the controller 𝐺𝐷(𝑧) do not cancel zeros of 𝐺 𝑧 which lies outside the unit circle. Therefore, if there is a zero (𝑧 βˆ’ 𝛽)of 𝐺(𝑧) which is outside the unit circle. Then, we can write 𝐺 𝑧 as follows: 𝐺 𝑧 = 𝑧 βˆ’ 𝛽 𝐺2(𝑧) Consequently, in case of (𝑧 βˆ’ 𝛽) is not cancelled: 𝐹 𝑧 = 𝐺𝐷 𝑧 𝑧 βˆ’ 𝛽 𝐺2(𝑧) 1 + 𝐺𝐷(𝑧) 𝑧 βˆ’ 𝛽 𝐺2(𝑧) Hence, 𝐹 𝛽 = 0 Direct control design - Analytical Method
  • 30. 5. An additional condition can be imposed to address steady-state accuracy requirements. In particular, if zero steady-state error due to a step input is required lim π‘˜β†’βˆž 𝑐 π‘˜π‘‡ = lim 𝑧→1 𝑧 βˆ’ 1 𝐹 𝑧 . 𝑧 𝑧 βˆ’ 1 = 1 Therefore, 𝐹 1 = 1 Direct control design - Analytical Method
  • 31. β€’ Steps of design 1.Select the desired settling time Ts and the desired maximum overshoot. 2.Select a suitable continuous-time closed-loop first- order or second-order closed-loop system with unit gain. 3.Obtain by converting the s-plane pole location to the z-plane pole location using pole-zero matching. 4.Verify that 𝐹(𝑧) meets the conditions for causality, stability, and steady-state error. If not, modify 𝐹(𝑧) until the conditions are met. Direct control design - Analytical Method
  • 32. β€’ EX6: Design a digital controller for a DC motor speed control system where the (type 0) analog plant has the transfer function 𝐺𝑝 = 1 (𝑠 + 1)(𝑠 + 10) To obtain zero steady-state error due to a unit step, πœ”π‘› = 1.15 π‘Ÿπ‘Žπ‘‘/𝑠, and 𝜁 = 0.88. The sampling period is chosen as 𝑇 = 0.02 s β€’ Solution Since in digital control system a ZOH is always inserted between the digital controller and the system to be controlled, hence 𝐺 𝑧 = π‘π‘‘π‘Ÿπ‘Žπ‘›π‘  1 βˆ’ π‘’βˆ’π‘ π‘‡ 𝑠 𝐺𝑝 𝑠 = (1 βˆ’ π‘§βˆ’1)π‘π‘‘π‘Ÿπ‘Žπ‘›π‘  𝐺𝑝 𝑠 𝑠 Direct control design - Analytical Method
  • 33. β€’ EX6 (cont.) 𝐺 𝑧 = 1.86 Γ— 10βˆ’4 𝑧 + 0.9293 (𝑧 βˆ’ 0.8187)(𝑧 βˆ’ 0.9802) Then, based on the requirements 𝐹 𝑠 is: 𝐹 𝑠 = πœ”π‘› 2 𝑠2 + 2πœπœ”π‘›π‘  + πœ”π‘› 2 = 1.322 𝑠2 + 2024𝑠 + 1.322 Using zero-pole matching 𝐹 𝑧 = 0.25921 Γ— 10βˆ’3 𝑧 + 1 𝑧2 βˆ’ 1.95981𝑧 + 0.96033 Direct control design - Analytical Method
  • 34. EX6(cont.): Then from 𝐺𝐷 𝑧 = 𝐹 𝑧 𝐺(𝑧) 1 βˆ’ 𝐹 𝑧 We get 𝐺𝐷 𝑧 = 1.3932(𝑧 βˆ’ 0.8187)(𝑧 βˆ’ 0.9802)(𝑧 + 1) (𝑧 βˆ’ 1)(𝑧 + 0.9293)(𝑧 βˆ’ 0.9601) Direct control design - Analytical Method