EHR ATTRIBUTE-BASED ACCESS CONTROL (ABAC) FOR FOG COMPUTING ENVIRONMENTcsandit
Liquid level tanks are employed in many industrial and chemical areas. Their level must be keep
a defined point or between maximum-minimum points depending on changing of inlet and outlet
liquid quantities. In order to overcome the problem, many level control methods have been
developed. In the paper, it was aimed that obtain a mathematical model of an installed liquid
level tank system. Then, the mathematical model was derived from the installed system
depending on the sizes of the liquid level tank. According to some proportional-integralderivative
(PID) parameters, the model was simulated by using MATLAB/Simulink program.
After that, data of the liquid level tank were taken into a computer by employing data
acquisition cards (DAQs). Lastly, the computer-controlled liquid level control was successfully
practiced through a written computer program embedded into a PID algorithm used the PID
parameters obtained from the simulations into Advantech VisiDAQ software
Dynamic Matrix Control (DMC) on jacket tank heater - Rishikesh BagweRishikesh Bagwe
The Dynamic Matrix Control (DMC) method of Model Predictive Control was simulated in MATLAB on Jacketed Tank Heater. The characteristics of the liquid being controlled are height and temperature
In this study the controller for three tank multi loop system is designed using coefficient Diagram
method. Coefficient Diagram Method is one of the polynomial methods in control design. The
controller designed by using CDM technique is based on the coefficients of the characteristics
polynomial of the closed loop system according to the convenient performance such as equivalent
time constant, stability indices and stability limit. Controller is designed for the three tank process
by using CDM Techniques; the simulation results show that the proposed control strategies have
good set point tracking and better response capability.
PID Controller Simulator Design for Polynomials Transfer FunctionMIbrar4
PID Controller Simulator Design for Polynomials Transfer Function
Objective:
• To compute responses at different values of PID for a transfer function for both open
loop and Closed Loop Simulation
COMPARATIVE ANALYSIS OF CONVENTIONAL PID CONTROLLER AND FUZZY CONTROLLER WIT...IJITCA Journal
All the real systems exhibits non-linear nature,conventional controllers are not always able to provide good and accurate results. Fuzzy Logic Control is used to obtain better response. A model for simulation is designed and all the assumptions are made before the development of the model. An attempt has been made to analyze the efficiency of a fuzzy controller over a conventional PID controller for a three tank level control system using fuzzification & defuzzification methods and their responses are compared. Analysis is done through computer simulation using Matlab/Simulink toolbox. This study shows that the application of Fuzzy Logic Controller (FLC) gives the best response with triangular membership function and centroid defuzzification method.
EHR ATTRIBUTE-BASED ACCESS CONTROL (ABAC) FOR FOG COMPUTING ENVIRONMENTcsandit
Liquid level tanks are employed in many industrial and chemical areas. Their level must be keep
a defined point or between maximum-minimum points depending on changing of inlet and outlet
liquid quantities. In order to overcome the problem, many level control methods have been
developed. In the paper, it was aimed that obtain a mathematical model of an installed liquid
level tank system. Then, the mathematical model was derived from the installed system
depending on the sizes of the liquid level tank. According to some proportional-integralderivative
(PID) parameters, the model was simulated by using MATLAB/Simulink program.
After that, data of the liquid level tank were taken into a computer by employing data
acquisition cards (DAQs). Lastly, the computer-controlled liquid level control was successfully
practiced through a written computer program embedded into a PID algorithm used the PID
parameters obtained from the simulations into Advantech VisiDAQ software
Dynamic Matrix Control (DMC) on jacket tank heater - Rishikesh BagweRishikesh Bagwe
The Dynamic Matrix Control (DMC) method of Model Predictive Control was simulated in MATLAB on Jacketed Tank Heater. The characteristics of the liquid being controlled are height and temperature
In this study the controller for three tank multi loop system is designed using coefficient Diagram
method. Coefficient Diagram Method is one of the polynomial methods in control design. The
controller designed by using CDM technique is based on the coefficients of the characteristics
polynomial of the closed loop system according to the convenient performance such as equivalent
time constant, stability indices and stability limit. Controller is designed for the three tank process
by using CDM Techniques; the simulation results show that the proposed control strategies have
good set point tracking and better response capability.
PID Controller Simulator Design for Polynomials Transfer FunctionMIbrar4
PID Controller Simulator Design for Polynomials Transfer Function
Objective:
• To compute responses at different values of PID for a transfer function for both open
loop and Closed Loop Simulation
COMPARATIVE ANALYSIS OF CONVENTIONAL PID CONTROLLER AND FUZZY CONTROLLER WIT...IJITCA Journal
All the real systems exhibits non-linear nature,conventional controllers are not always able to provide good and accurate results. Fuzzy Logic Control is used to obtain better response. A model for simulation is designed and all the assumptions are made before the development of the model. An attempt has been made to analyze the efficiency of a fuzzy controller over a conventional PID controller for a three tank level control system using fuzzification & defuzzification methods and their responses are compared. Analysis is done through computer simulation using Matlab/Simulink toolbox. This study shows that the application of Fuzzy Logic Controller (FLC) gives the best response with triangular membership function and centroid defuzzification method.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Design of Multiloop Controller for Three Tank Process Using CDM Techniques ijsc
In this study the controller for three tank multi loop system is designed using coefficient Diagram method. Coefficient Diagram Method is one of the polynomial methods in control design. The controller designed by using CDM technique is based on the coefficients of the characteristics polynomial of the closed loop system according to the convenient performance such as equivalent time constant, stability indices and stability limit. Controller is designed for the three tank process by using CDM Techniques; the simulation results show that the proposed control strategies have good set point tracking and better response capability.
Why Digital Control?
Advantages of Digital Control System
INTRODUCTION TO DIGITAL CONTROL
The structure of a digital control system
Examples of digital control systems
Closed-loop drug delivery system
Computer control of an aircraft turbojet engine
Analysis and Modeling of PID and MRAC Controllers for a Quadruple Tank System...dbpublications
Multivariable systems exhibit complex dynamics because of the interactions between input variables and output variables. In this paper an approach to design auto tuned decentralized PI controller using ideal decoupler and adaptive techniques for controlling a class of multivariable process with a transmission zero. By using decoupler, the MIMO system is transformed into two SISO systems. The controller parameters were adjusted using the Model Reference Adaptive reference Control. In recent process industries, PID and MRAC are the two widely accepted control strategies, where PID is used at regulatory level control and MRAC at supervisory level control. In this project, LabVIEW is used to simulate the PID with Decoupler and MRAC separately and analyze their performance based on steady state error tracking and overshoot.
Output feedback trajectory stabilization of the uncertainty DC servomechanism...ISA Interchange
This work proposes a solution for the output feedback trajectory-tracking problem in the case of an uncertain DC servomechanism system. The system consists of a pendulum actuated by a DC motor and subject to a time-varying bounded disturbance. The control law consists of a Proportional Derivative controller and an uncertain estimator that allows compensating the effects of the unknown bounded perturbation. Because the motor velocity state is not available from measurements, a second-order sliding-mode observer permits the estimation of this variable in finite time. This last feature allows applying the Separation Principle. The convergence analysis is carried out by means of the Lyapunov method. Results obtained from numerical simulations and experiments in a laboratory prototype show the performance of the closed loop system.
Due to extensive use of motion control system in industry, there has been growing research on proportional-integral-derivative (PID) controllers. DC motors are widely used various areas of industrial applications. The aim of this paper is to implement efficient method for controlling speed of DC motor using a PID controller based. Proposed system is implemented using arduino microcontroller and PID controller. Motor speed is controlled through PID based revolutions per minute of the motor. This encoder data will be send through microcontroller to Personal Computer with PID controller implemented in MATLAB. Results shows that PID controllers used provide efficient controlling of DC motor.
A Robust Fuzzy Logic Control of Two Tanks Liquid Level ProcessINFOGAIN PUBLICATION
An attempt has been made in this paper to analyze the efficiency of Fuzzy Logic, PID controllers on Non Interacting Two Tanks (Cylindrical) Liquid Level Process. The liquid level process exhibits Nonlinear square root law flow characteristics. The control problem formulated as level in second tank is controlled variable and the inlet flow to the first tank is manipulated variable. The PID Controller is designed based on Internal Model Control (IMC) Method. The Artificial Intelligent Fuzzy logic controller is designed based on six rules with Gaussian and triangular fuzzy sets. MATLAB - Simulink has been used to simulate and verified the mathematical model of the controller. Simulation Results show that the proposed Fuzzy Logic Controller show robust performance with faster response and no overshoot, where as the conventional PID Controller shows oscillations responses for set point changes. Thus, the Artificial Intelligent FLC is founded to give superior performance for a Non linear problem like two tanks. This paper will help the method suitable for research findings concerning on two tank liquid level system.
Evaluation of the stability enhancement of the conventional sliding mode cont...nooriasukmaningtyas
The proposed work is an attempt to investigate the stability of the nonlinear
system by using a whale optimization algorithm as of one of the metaheuristic optimization methods, and this investigation was conducted on a
single inverted pendulum as a study model. The evaluation measures which
were used in this article values of gain and sliding surface of the
conventional sliding mode controller to illustrate the extent of the system`s
stability. Furthermore, control action, the relationship between error and its
derivative, desired, and actual position in addition to sliding response
graphically showed the feasibility of the proposed solution. The attained
results illustrated considerable improvement in the settling time and
minimizing the impact of chattering behavior.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Design of Multiloop Controller for Three Tank Process Using CDM Techniques ijsc
In this study the controller for three tank multi loop system is designed using coefficient Diagram method. Coefficient Diagram Method is one of the polynomial methods in control design. The controller designed by using CDM technique is based on the coefficients of the characteristics polynomial of the closed loop system according to the convenient performance such as equivalent time constant, stability indices and stability limit. Controller is designed for the three tank process by using CDM Techniques; the simulation results show that the proposed control strategies have good set point tracking and better response capability.
Why Digital Control?
Advantages of Digital Control System
INTRODUCTION TO DIGITAL CONTROL
The structure of a digital control system
Examples of digital control systems
Closed-loop drug delivery system
Computer control of an aircraft turbojet engine
Analysis and Modeling of PID and MRAC Controllers for a Quadruple Tank System...dbpublications
Multivariable systems exhibit complex dynamics because of the interactions between input variables and output variables. In this paper an approach to design auto tuned decentralized PI controller using ideal decoupler and adaptive techniques for controlling a class of multivariable process with a transmission zero. By using decoupler, the MIMO system is transformed into two SISO systems. The controller parameters were adjusted using the Model Reference Adaptive reference Control. In recent process industries, PID and MRAC are the two widely accepted control strategies, where PID is used at regulatory level control and MRAC at supervisory level control. In this project, LabVIEW is used to simulate the PID with Decoupler and MRAC separately and analyze their performance based on steady state error tracking and overshoot.
Output feedback trajectory stabilization of the uncertainty DC servomechanism...ISA Interchange
This work proposes a solution for the output feedback trajectory-tracking problem in the case of an uncertain DC servomechanism system. The system consists of a pendulum actuated by a DC motor and subject to a time-varying bounded disturbance. The control law consists of a Proportional Derivative controller and an uncertain estimator that allows compensating the effects of the unknown bounded perturbation. Because the motor velocity state is not available from measurements, a second-order sliding-mode observer permits the estimation of this variable in finite time. This last feature allows applying the Separation Principle. The convergence analysis is carried out by means of the Lyapunov method. Results obtained from numerical simulations and experiments in a laboratory prototype show the performance of the closed loop system.
Due to extensive use of motion control system in industry, there has been growing research on proportional-integral-derivative (PID) controllers. DC motors are widely used various areas of industrial applications. The aim of this paper is to implement efficient method for controlling speed of DC motor using a PID controller based. Proposed system is implemented using arduino microcontroller and PID controller. Motor speed is controlled through PID based revolutions per minute of the motor. This encoder data will be send through microcontroller to Personal Computer with PID controller implemented in MATLAB. Results shows that PID controllers used provide efficient controlling of DC motor.
A Robust Fuzzy Logic Control of Two Tanks Liquid Level ProcessINFOGAIN PUBLICATION
An attempt has been made in this paper to analyze the efficiency of Fuzzy Logic, PID controllers on Non Interacting Two Tanks (Cylindrical) Liquid Level Process. The liquid level process exhibits Nonlinear square root law flow characteristics. The control problem formulated as level in second tank is controlled variable and the inlet flow to the first tank is manipulated variable. The PID Controller is designed based on Internal Model Control (IMC) Method. The Artificial Intelligent Fuzzy logic controller is designed based on six rules with Gaussian and triangular fuzzy sets. MATLAB - Simulink has been used to simulate and verified the mathematical model of the controller. Simulation Results show that the proposed Fuzzy Logic Controller show robust performance with faster response and no overshoot, where as the conventional PID Controller shows oscillations responses for set point changes. Thus, the Artificial Intelligent FLC is founded to give superior performance for a Non linear problem like two tanks. This paper will help the method suitable for research findings concerning on two tank liquid level system.
Evaluation of the stability enhancement of the conventional sliding mode cont...nooriasukmaningtyas
The proposed work is an attempt to investigate the stability of the nonlinear
system by using a whale optimization algorithm as of one of the metaheuristic optimization methods, and this investigation was conducted on a
single inverted pendulum as a study model. The evaluation measures which
were used in this article values of gain and sliding surface of the
conventional sliding mode controller to illustrate the extent of the system`s
stability. Furthermore, control action, the relationship between error and its
derivative, desired, and actual position in addition to sliding response
graphically showed the feasibility of the proposed solution. The attained
results illustrated considerable improvement in the settling time and
minimizing the impact of chattering behavior.
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Lecture _1_ Digital Control Systems.pptx
1. DIGITAL CONTROL SYSTEMS
LECTURE NO.1
PROF. DR. KHALAF S GAEID
ELECTRICAL ENGINEERING DEPARTMENT/ TIKRIT
UNIVERSITY
GAEIDKHALAF@GMAIL.COM
2. 1. A control system consisting of interconnected components is designed to achieve a desired
purpose.
2. Modern control engineering practice includes the use of control design strategies for
improving manufacturing processes, the efficiency of energy use, advanced automobile
control, including rapid transit, among others.
3. We also discuss the notion of a design gap. The gap exists between the complex physical
system under investigation and the model used in the control system synthesis.
4. The iterative nature of design allows us to handle the design gap effectively while
accomplishing necessary tradeoffs in complexity, performance, and cost in order to meet
the design specifications.
Introduction to Control Systems Objectives
3. Introduction
System – An interconnection of elements and devices for a desired purpose.
Control System – An interconnection of components forming a system configuration that
will provide a desired response.
Process – The device, plant, or system under control. The input and output relationship
represents the cause-and-effect relationship of the process.
4. What is Digital Control?
Automatic control is the science that develops techniques to steer, guide, control dynamic
systems.
These systems are built by humans and must perform a specific task. Examples of such
dynamic systems are found in biology, physics, robotics, finance, etc.
Digital Control means that the control laws are implemented in a digital device, such as a
microcontroller or a microprocessor. Such devices are light, fast and economical.
Digital Control Systems z-Plane Analysis of Discrete Time Control Systems
5. INTRODUCTION
Digital control offers distinct advantages over analog control that explain its
popularity.
Accuracy: Digital signals are more accurate than their analogue counterparts.
Implementation Errors: Implementation errors are negligible.
Flexibility: Modification of a digital controller is possible without complete
replacement.
Speed: Digital computers may yield superior performance at very fast speeds
Cost: Digital controllers are more economical than analogue controllers.
5
6. DISADVANTAGES OF DIGITAL COMPUTERS
FROM THE TRACKING PERFORMANCE SIDE, THE ANALOG CONTROL SYSTEM
EXHIBITS GOOD PERFORMANCES THAN DIGITAL CONTROL SYSTEM.
DIGITAL CONTROL SYSTEM WILL INTRODUCE A DELAY IN THE LOOP.
6
https://www.mathworks.com/academia/books/search.html?q=digital%20control&
page=1
8. Examples of Digitally Controlled Systems
Nowadays, digitally controlled systems are everywhere,
• Automotive industry: speed regulators in cars,
• Aeronautic/space industry: autopilots, automatic take off/landing, cruise control
• Chemistry: pharmaceutical industries, oil transformation, liquid level in tanks
• Robotics: robot-arm trajectory control, manipulation,
• Housing: in-house temperature regulation
9. INTRODUCTION
Modeling of dynamic systems
Model: A representation of a system.
Types of Models:
1. Physical models (prototypes)
2. Mathematical models (e.g., input-output relationships)
3. Analytical models (using physical laws)
4. Computer (numerical) models
5. Experimental models (using input/output experimental data)
Models for physical dynamic systems: Lumped-parameter models
Continuous-parameter models. Example: Spring element (flexibility, inertia, damping)
10. INTRODUCTION
Signal categories for identifying control system types
Continuous-time signal & quantized signal
Continuous-time signal is defined continuously in the time domain. Figure on the left
shows a continuous-time signal, represented by x(t).
Quantized signal is a signal whose amplitudes are discrete and limited. Figure on the
right shows a quantized signal.
Analog signal or continuous signal is continuous in time and in amplitude. The real
word consists of analog signals.
The Simulink quantizers as
11. INTRODUCTION
Discrete-time signal & sampled-data signal
Discrete-time signal is defined only at certain time instants. For a discrete-time signal, the
amplitude between two consecutive time instants is just not defined. Figure on the left shows a
discrete-time signal, represented by y(kh), or simply y(k), where k is an integer and h is the time
interval.
Sampled-data signal is a discrete-time signal resulting by sampling a continuous-time signal.
Figure on the right shows a sampled-data signal deriving from the continuous-time signal, shown
in the figure at the center, by a sampling process. It is represented by x
∗
(t).
Discrete time signal sampled data signal
12. INTRODUCTION
Digital signal or binary coded data signal
Digital signal is a sequence of binary numbers. In or out from a microprocessor, a
semiconductor memory, or a shift register.
In practice, a digital signal, as shown in the figures at the bottom, is derived by two
processes: sampling and then quantizing.
15. INTRODUCTION
Controller design in digital control systems - Design in S-domain
Digitization (DIG) or discrete control design
The above design works very well if sampling period T is sufficiently small.
16. INTRODUCTION
How to design a Controller:
For the approximation methods, used to convert the continuous controller to
digital controller, are: Euler method, Trapezoidal method …etc and we can use
forward or backward approximation.
Afterwards, the differential equation of the controller can be transformed to
difference equation where this difference equation can be easily programmed as a
control algorithm.
Note that the sampling time T should be close to zero.
Hw. Design digital controller using simulink
24. DIFFERENCE EQUATION VS DIFFERENTIAL EQUATION
A difference equation expresses the change in some variable as a result of a
finite change in another variable.
A differential equation expresses the change in some variable as a result of an
infinitesimal change in another variable.
24
27. DIFFERENCE EQUATIONS
Example-1: For each of the following difference equations, determine the (a) order
of the equation. Is the equation (b) linear, (c) time invariant, or (d) homogeneous?
1. 𝑦 𝑘 + 2 + 0.8𝑦 𝑘 + 1 + 0.07𝑦 𝑘 = 𝑢 𝑘
2. 𝑦 𝑘 + 4 + sin(0.4𝑘)𝑦 𝑘 + 1 + 0.3𝑦 𝑘 = 0
3. 𝑦 𝑘 + 1 = −0.1𝑦2
𝑘
Try to implement all the above difference equations with simulink as HW
27
28. DIFFERENCE EQUATIONS
Example-1: For each of the following difference equations, determine the
(a) order of the equation. Is the equation (b) linear, (c) time invariant, or (d)
homogeneous?
1. 𝑦 𝑘 + 2 + 0.8𝑦 𝑘 + 1 + 0.07𝑦 𝑘 = 𝑢 𝑘
Solution:
a) The equation is second order.
b) All terms enter the equation linearly
c) All the terms if the equation have constant coefficients. Therefore the
equation is therefore LTI.
d) A forcing function appears in the equation, so it is nonhomogeneous.
28
29. DIFFERENCE EQUATIONS
Example-1: For each of the following difference equations, determine the
(a) order of the equation. Is the equation (b) linear, (c) time invariant, or (d)
homogeneous?
2. 𝑦 𝑘 + 4 + sin(0.4𝑘)𝑦 𝑘 + 1 + 0.3𝑦 𝑘 = 0
Solution:
a) The equation is 4th order.
b) All terms are linear
c) The second coefficient is time dependent
d) There is no forcing function therefore the equation is homogeneous.
29
30. DIFFERENCE EQUATIONS
Example-1: For each of the following difference equations, determine the
(a) order of the equation. Is the equation (b) linear, (c) time invariant, or
(d) homogeneous?
3. 𝑦 𝑘 + 1 = −0.1𝑦2
𝑘
Solution:
a) The equation is 1st order.
b) Nonlinear
c) Time invariant
d) Homogeneous
30
31. FIGURE 2A. PLACEMENT OF THE DIGITAL COMPUTER WITHIN THE LOOP; B. DETAILED BLOCK
DIAGRAM SHOWING PLACEMENT OF A/D AND D/A CONVERTERS
43. CONFORMAL MAPPING BETWEEN S-PLANE TO Z-PLANE
Where 𝑠 = 𝜎 + 𝑗𝜔.
Then 𝑧 in polar coordinates is given by
43
𝑧 = 𝑒(𝜎+𝑗𝜔)𝑇
𝑧 = 𝑒𝜎𝑇
𝑒𝑗𝜔𝑇
∠𝑧 = 𝜔𝑇
𝑧 = 𝑒𝜎𝑇
𝑧 = 𝑒𝑠𝑇
44. CONFORMAL MAPPING BETWEEN S-PLANE TO Z-PLANE
We will discuss following cases to map given points on s-plane to z-plane.
Case-1: Real pole in s-plane (𝑠 = 𝜎)
Case-2: Imaginary Pole in s-plane (𝑠 = 𝑗𝜔)
Case-3: Complex Poles (𝑠 = 𝜎 + 𝑗𝜔)
44
𝑠 − 𝑝𝑙𝑎𝑛𝑒 𝑧 − 𝑝𝑙𝑎𝑛𝑒
45. CONFORMAL MAPPING BETWEEN S-PLANE TO Z-PLANE
Case-1: Real pole in s-plane (𝑠 = 𝜎)
We know
Therefore
45
∠𝑧 = 𝜔𝑇
𝑧 = 𝑒𝜎𝑇
𝑧 = 𝑒𝜎𝑇 ∠𝑧 = 0
46. CONFORMAL MAPPING BETWEEN S-PLANE TO Z-PLANE
46
When 𝑠 = 0
∠𝑧 = 𝜔𝑇
𝑧 = 𝑒𝜎𝑇
𝑧 = 𝑒0𝑇 = 1
∠𝑧 = 0𝑇 = 0
𝑠 = 0
𝑠 − 𝑝𝑙𝑎𝑛𝑒 𝑧 − 𝑝𝑙𝑎𝑛𝑒
1
Case-1: Real pole in s-plane (𝑠 = 𝜎)
47. CONFORMAL MAPPING BETWEEN S-PLANE TO Z-PLANE
47
When 𝑠 = −∞
∠𝑧 = 𝜔𝑇
𝑧 = 𝑒𝜎𝑇
𝑧 = 𝑒−∞𝑇
= 0
∠𝑧 = 0
−∞
𝑠 − 𝑝𝑙𝑎𝑛𝑒 𝑧 − 𝑝𝑙𝑎𝑛𝑒
0
Case-1: Real pole in s-plane (𝑠 = 𝜎)
48. CONFORMAL MAPPING BETWEEN S-PLANE TO Z-PLANE
Case-2: Imaginary pole in s-plane (𝑠 = ±𝑗𝜔)
We know
Therefore
48
∠𝑧 = 𝜔𝑇
𝑧 = 𝑒𝜎𝑇
𝑧 = 1 ∠𝑧 = ±𝜔𝑇
52. FIG.14.FINDING STABILITY OF A MISSILE CONTROL SYSTEM:
A. MISSILE; B. CONCEPTUAL BLOCK DIAGRAM; C. BLOCK DIAGRAM;
D. BLOCK DIAGRAM WITH EQUIVALENT SINGLE SAMPLER
53. FIG.25 A. DIGITAL CONTROL SYSTEM SHOWING THE DIGITAL COMPUTER PERFORMING COMPENSATION;
B. CONTINUOUS SYSTEM USED FOR DESIGN;
C. TRANSFORMED DIGITAL SYSTEM
55. 55
DISCRETE-TIME SIGNALS: SEQUENCES
Discrete-Time signals are represented as
In sampling of an analog signal xa(t):
1/T (reciprocal of T) : sampling frequency
integer
:
,
, n
n
n
x
x
period
sampling
T
nT
x
n
x a :
,
10/5/2023
Cumbersome, so just use
x n
difficult to do or manage and taking a lot
of time and effort
56. 56
FIGURE. GRAPHICAL REPRESENTATION OF A DISCRETE-TIME SIGNAL
10/5/2023
56
Abscissa: continuous line
: is defined only at discrete instants
x n
58. 58 10/5/2023
58
Sum of two sequences
Product of two sequences
Multiplication of a sequence by a number α
Delay (shift) of a sequence
BASIC SEQUENCE OPERATIONS
]
[
]
[ n
y
n
x
integer
:
]
[
]
[ 0
0 n
n
n
x
n
y
]
[
]
[ n
y
n
x
]
[n
x
59. 59
BASIC SEQUENCES
Unit sample sequence (discrete-time impulse, impulse, Unit
impulse)
0
1
0
0
n
n
n
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59
continuous-time unit impulse function δ(t) )
60. 60
BASIC SEQUENCES
[ ] [ ] [ ]
k
x n x k n k
7
2
1
3 7
2
1
3
n
a
n
a
n
a
n
a
n
p
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60
arbitrary sequence
A sum of scaled, delayed impulses
61. 1. The acronym DCS stands for?
2. Many digital control systems utilize Ethernet as a communications network, because . .
3.Resolution refers to in the analog-to-digital conversion portion of a digital control system.
4,Parity bits are used for the purpose of in digital
systems.
5.A typical use for an integer variable in a digital control
system is:
6.A watchdog timer is a device or a programmed routine used for what purpose in a digital
control system?
Projects
Design Efficient DC-to-DC Power Converters
Electric vehicles and charging stations
Renewable energy
Convert SPICE models into Simscape components
Convert SPICE models into Simscape components
Implement Power Electronics Control on an Embedded Platform
Replace Hand Coding with Code Generation
Optimize for C2000 to Improve Execution Performance
Strategies Hardware-in-the-Loop (HIL) Testing
FPGA-based HIL Relevant to Power Electronics
Adaptive control for stability optimization for Induction Mo
Intelligent control for stability optimization for Induction M
Intelligent robotic control for stability optimization