- The document discusses linear time-invariant (LTI) systems and their representations in the time domain.
- It covers various properties of LTI systems including parallel and cascade connections, causality, stability, and memory.
- Methods for representing LTI systems using impulse responses, differential/difference equations, and step responses are presented.
- Solving techniques for determining the homogeneous and particular solutions of LTI systems described by differential or difference equations are outlined.
z-Transform is for the analysis and synthesis of discrete-time control systems.The z transform in discrete-time systems play a similar role as the Laplace transform in continuous-time systems
This presentation gives the basic idea about the methods of solving ODEs
The methods like variation of parameters, undetermined coefficient method, 1/f(D) method, Particular integral and complimentary functions of an ODE
It gives how states are representing in various canonical forms and how it it is different from transfer function approach. and finally test the system controllability and observability by kalman's test
z-Transform is for the analysis and synthesis of discrete-time control systems.The z transform in discrete-time systems play a similar role as the Laplace transform in continuous-time systems
This presentation gives the basic idea about the methods of solving ODEs
The methods like variation of parameters, undetermined coefficient method, 1/f(D) method, Particular integral and complimentary functions of an ODE
It gives how states are representing in various canonical forms and how it it is different from transfer function approach. and finally test the system controllability and observability by kalman's test
Here's the continuation of the report:
3.2.1 Parallel Plate Capacitor (continued)
As the IV fluid droplets move between the plates of the capacitor, the capacitance increases due to the change in the dielectric constant, resulting in the observation of a peak in capacitance.
3.2.2 Semi-cylindrical Capacitor
The semi-cylindrical capacitor consists of two semi-cylindrical conductors (plates) facing each other with a gap between them. The gap between the plates is filled with a dielectric material, typically the IV fluid.
When a potential difference is applied across the plates, electric field lines form between them. The dielectric material between the plates enhances the capacitance by reducing the electric field strength and increasing the charge storage capacity.
3.2.3 Cylindrical Cross Capacitor
The cylindrical cross capacitor is composed of two cylindrical conductors (rods) intersecting at right angles to form a cross shape. The space between the rods is filled with a dielectric material, such as the IV fluid.
When a potential difference is applied between the rods, electric field lines form between them. The dielectric material between the rods enhances the capacitance by reducing the electric field strength and increasing the charge storage capacity, similar to the semi-cylindrical design.
3.3 Advantages of Capacitive Sensing Approach
Capacitive sensing for IV fluid monitoring offers several advantages over other automated monitoring methods:
1. Non-invasive operation: The sensors do not require direct contact with the IV fluid, reducing the risk of contamination or disruption to the therapy.
2. High sensitivity: Capacitive sensors can detect minute changes in capacitance, enabling precise tracking of IV fluid droplets.
3. Low cost: The sensors can be constructed using relatively inexpensive materials, making them a cost-effective solution.
4. Low power consumption: Capacitive sensors typically have low power requirements, making them suitable for continuous monitoring applications.
5. Ease of implementation: The sensors can be easily integrated into existing IV setups without significant modifications.
6. Stable measurements: Capacitive sensors can provide stable and repeatable measurements across different IV fluid types.
Chapter 4: Experimental Setup and Results
4.1 Description of Experimental Setup
To evaluate the performance of capacitive sensors for IV fluid monitoring, an experimental setup was constructed. The setup included various capacitive sensor designs, such as parallel plate, semi-cylindrical, and cylindrical cross capacitors, positioned around an IV drip chamber.
The sensors were connected to a capacitance measurement circuit, which recorded the changes in capacitance as IV fluid droplets passed through the sensor's electric field. Multiple experiments were conducted using different IV fluid types and flow rates to assess the sensors' accuracy, repeatability, and sensitivity.
4.2 Measurements with
Generic Reinforcement Schemes and Their Optimizationinfopapers
Dana Simian, Florin Stoica, Generic Reinforcement Schemes and Their Optimization, Proceedings of the 5th European Computing Conference (ECC ’11), Paris, France, April 28-30, 2011, pp. 332-337
A new Reinforcement Scheme for Stochastic Learning Automatainfopapers
F. Stoica, E. M. Popa, I. Pah, A new reinforcement scheme for stochastic learning automata – Application to Automatic Control, Proceedings of the International Conference on e-Business, Porto, Portugal, ISBN 978-989-8111-58-6, pp. 45-50, July 2008
The postulates of quantum mechanics have been successfully used for deriving exact solutions to Schrodinger equation for problems like A particle in 1 Dimensional box Harmonic oscillator Rigid rotator Hydrogen atom • However for a multielectron system, the SWE cannot be solved exactly due to inter-electronic repulsion terms.
The SWE is solved by method of seperation of variables.
• However, the inter-electronic repulsion term cannot be solved because the variables cannot be seperated and the SWE cannot be solved. • Approximate methods have helped to generate solutions for such and even more complex real quantum systems. • Approximate methods have been developed for solving Schrodinger equation to find wave function and energy of the complex system under consideration. • Two widely used approximate methods are, 1. Perturbation theory 2. Variation method
Perturbation theory is an approximate method that describes a complex quantum system in terms of a simpler system for which the exact solution is known. • Perturbation theory has been categorized into, i. Time independent perturbation theory, proposed by Erwin Schrodinger, where the perturbation Hamiltonian is static. ii. Time dependent perturbation theory, proposed by Paul Dirac, which studies the effect of time dependent perturbation on a time independent Hamiltonian H0.
PERTURBATION THEOREM
FIRST ORDER PERTURBATION THEORY
FIRST ORDER ENERGY CORRECTION
FIRST ORDER WAVE FUNCTION CORRECTION
APPLICATIONS OF PERTURBATION METHOD
SIGNIFICANCE OF PERTURBATION METHOD
We give an elementary exposition of a method to obtain the infinitesimal point symmetries of Lagrangians.Besides, we exhibit the Lanczos approach to Noether’s theorem to construct the first integral associated with each symmetry.
MSC 2010:49S05, 58E30, 70H25, 70H33
Optimizing a New Nonlinear Reinforcement Scheme with Breeder genetic algorithminfopapers
Florin Stoica, Dana Simian, Optimizing a New Nonlinear Reinforcement Scheme with Breeder genetic algorithm, Proceedings of the Recent Advances in Neural Networks, Fuzzy Systems & Evolutionary Computing,13-15 June 2010, Iasi, Romania, ISSN: 1790-2769, ISBN: 978-960-474-194-6, pp. 273-278
A new Evolutionary Reinforcement Scheme for Stochastic Learning Automatainfopapers
F. Stoica, E. M. Popa, A new Evolutionary Reinforcement Scheme for Stochastic Learning Automata, Proceedings of the 12th WSEAS International Conference on COMPUTERS, Heraklion, Greece, July 23-25, ISBN: 978-960-6766-85-5, ISSN: 1790-5109, pp. 268-273, 2008
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
3. Interconnection of LTI Systems
Distributive Property for CT case:
Distributive property for DT case:
Cascade Connection of LTI systems
Prof: Sarun Soman, MIT, Manipal 3
4. Interconnection of LTI Systems
Interconnection Properties of LTI Systems
Prof: Sarun Soman, MIT, Manipal 4
5. Interconnection of LTI Systems
Relation between LTI system properties and the impulse
response.
Memory less LTI system
The o/p of a DT LTI system
To be memoryless y[n] must depend only on present value of
input
The o/p cannot depend on x[n-k] for ݇ ≠ 0
ݕ ݊ = ℎ ݇ ݊[ݔ − ݇]
ஶ
ୀିஶ
Prof: Sarun Soman, MIT, Manipal 5
6. Time Domain Representations of LTI Systems
A DT LTI system is memoryless if and only if
ℎ ݇ = ܿߜ[݇] ‘c’ is an arbitrary constant
CT system
Output
A CT LTI system is memoryless if and only if
ℎ ߬ = ܿߜ(߬) ‘c’ is an arbitrary constant
ݕ ݐ = න ℎ ߬ ݔ ݐ − ߬ ݀߬
ஶ
ିஶ
Prof: Sarun Soman, MIT, Manipal 6
7. Time Domain Representations of LTI Systems
Causal LTI systems
The o/p of a causal LTI system depends only on past or present
values of the input
DT system
ݕ ݊ = ℎ ݇ ݊[ݔ − ݇]
ஶ
ୀିஶ
future value of inputs
Prof: Sarun Soman, MIT, Manipal 7
8. Time Domain Representations of LTI Systems
For a DT causal LTI system
ℎ ݇ = 0 ݂݇ ݎ < 0
Convolution Sum in new form
For CT system
ℎ ߬ = 0 ݂߬ ݎ < 0
Convolution integral in new form
ݕ ݊ = ℎ ݇ ݊[ݔ − ݇]
ஶ
ୀ
ݕ ݐ = න ℎ ߬ ݔ ݐ − ߬ ݀߬
ஶ
Prof: Sarun Soman, MIT, Manipal 8
9. Time Domain Representations of LTI Systems
Stable LTI Systems
A system is BIBO stable if the o/p is guaranteed to be bounded
for every i/p.
Discrete time case:
Bounding convolution sum
Prof: Sarun Soman, MIT, Manipal 9
10. Time Domain Representations of LTI Systems
Assume that i/p is bounded
Condition for impulse response of a stable DT LTI system
Prof: Sarun Soman, MIT, Manipal 10
11. Time Domain Representations of LTI Systems
Continuous time case
Impulse response must be absolutely integrable.
න ℎ ߬ ݀߬ < ∞
ஶ
ିஶ
impulse response must be absolutely summable.
Prof: Sarun Soman, MIT, Manipal 11
15. Time Domain Representations of LTI Systems
Step Response
Characterizes the response to sudden changes in input.
ݏ ݊ = ℎ ݊ ∗ ]݊[ݑ
u[n-k] exist from −∞ to n
h[k] is the running sum of impulse response.
= ℎ ݇ ݊[ݑ − ݇]
ஶ
ୀିஶ
]݊[ݏ = ℎ ݇
ୀିஶ
Prof: Sarun Soman, MIT, Manipal 15
16. Time Domain Representations of LTI Systems
CT system
Relation b/w step response and impulse response.
ℎ ݐ =
݀
݀ݐ
)ݐ(ݏ
ℎ ݊ = ݏ ݊ − ݊[ݏ − 1]
Eg. Step response of an RC ckt
ݏ ݐ = න ℎ ߬ ݀߬
௧
ିஶ
Prof: Sarun Soman, MIT, Manipal 16
20. Differential and Difference equation
Representations of LTI Systems
Linear constant coefficient differential equation:
Linear constant coefficient difference equation:
Order of the equation is (N,M), representing the number of
energy storage devices in the system.
Often N>M and the order is described using only ‘N’.
ܽ
݀
݀ݐ
ݕ ݐ = ܾ
݀
݀ݐ
)ݐ(ݔ
ெ
ୀ
ே
ୀ
ܽ݊[ݕ − ݇] = ܾ݊[ݔ − ݇]
ெ
ୀ
ே
ୀ
Prof: Sarun Soman, MIT, Manipal 20
22. Differential and Difference equation
Representations of LTI Systems
Second order difference equation:
Difference equation are easily arranged to obtain recursive
formulas for computing the current o/p of the system.
(1) Shows how to obtain y[n] from present and past values of
the input.
ܽ݊[ݕ − ݇] = ܾ݊[ݔ − ݇]
ெ
ୀ
ே
ୀ
ݕ ݊ =
1
ܽ
ܾ݊[ݔ − ݇]
ெ
ୀ
−
1
ܽ
ܽݕ ݊ − ݇
ே
ୀଵ
(1)
Prof: Sarun Soman, MIT, Manipal 22
23. Differential and Difference equation
Representations of LTI Systems
Recursive evaluation of a difference equation.
Find the first two o/p values y[0] and y[1] for the system
described by ݕ ݊ = ݔ ݊ + 2ݔ ݊ − 1 − ݕ ݊ − 1 −
ଵ
ସ
݊[ݕ − 2].
Assuming that the input is ݔ ݊ = (
ଵ
ଶ
)ݑ ݊ and the initial
conditions are y[-1]=1 and y[-2]=-2.
Prof: Sarun Soman, MIT, Manipal 23
24. Solving Differential and Difference
Equations
Output of LTI system described by differential or difference
equation has two components
ݕ
- homogeneous solution
ݕ- particular solution
Complete solution
ݕ = ݕ + ݕ
Eg.
Prof: Sarun Soman, MIT, Manipal 24
25. Solving Differential and Difference
Equations
Output due to non zero initial conditions with zero input
Prof: Sarun Soman, MIT, Manipal 25
26. Solving Differential and Difference
Equations
Step response, system initially at rest.
Prof: Sarun Soman, MIT, Manipal 26
27. Solving Differential and Difference
Equations
The Homogenous Solution
CT system
Set all terms involving input to zero
Solution
ݎare the N roots of the characteristic equation
ܽ
݀
݀ݐ
ݕ ݐ = ܾ
݀
݀ݐ
)ݐ(ݔ
ெ
ୀ
ே
ୀ
ܽ
݀
݀ݐ
ݕ ݐ = 0
ே
ୀ
ݕ()ݐ = ܿ݁௧
ே
ୀଵ
Prof: Sarun Soman, MIT, Manipal 27
28. Solving Differential and Difference
Equations
DT system
Set all terms involving input to zero
Solution
ݎ are the N roots of the characteristic equation.
ܽ݊[ݕ − ݇] = ܾ݊[ݔ − ݇]
ெ
ୀ
ே
ୀ
ܽ݊[ݕ − ݇] = 0
ே
ୀ
ݕ[݊] = ܿݎ
ே
ୀଵ
Prof: Sarun Soman, MIT, Manipal 28
29. Solving Differential and Difference
Equations
If the roots are repeating ‘p’ times then there are ‘p’ distinct
terms
݁௧, ݁ݐ௧, … . ݐିଵ݁௧
and ݎ
, ݊ݎ
… . . ݊ିଵݎ
Eg.
RC ckt depicted in figure is described by the differential equation
ݕ ݐ + ܴܥ
ௗ
ௗ௧
ݕ ݐ = )ݐ(ݔ . Determine the homogenous solution.
Prof: Sarun Soman, MIT, Manipal 29
30. Solving Differential and Difference
Equations
The homogenous equation is
ݕ ݐ + ܴܥ
ௗ
ௗ௧
ݕ ݐ = 0 (1)
Solution
ݕ
ݐ = ܿଵ݁భ௧
To determine ݎଵsubstitute in (1)
ݕ ݐ + ܴܥ
݀
݀ݐ
ݕ ݐ = 0
ܿଵ݁భ௧ 1 + ܴݎܥଵ = 0
ܿଵ݁భ௧ ≠ 0
Characteristic equation
1 + ܴݎܥଵ = 0
ݎଵ = −
1
ܴܥ
Homogenous solution of the system
is
ݕ ݐ = ܿଵ݁ି
ଵ
ோ௧
Note: ܿଵ is determined later, in order
that the complete solution satisfy
the initial conditions.
ݕ
()ݐ = ܿ݁௧
ே
ୀଵ
Prof: Sarun Soman, MIT, Manipal 30
35. Solving Differential and Difference
Equations
The Particular Solution ݕ
Solution of difference or differential equation for a given input.
Assumption : output is of same general form as the input.
CT System
Input Particular solution
1 c
t ܿଵݐ + ܿଶ
݁ି௧
ܿ݁ି௧
cos(⍵ݐ + ⏀) ܿଵ cos ߱ݐ + ܿଶ sin(߱)ݐ
DT System
Input Particular solution
1 c
n ܿଵ݊ + ܿଶ
ߙ ܿߙ
cos(Ω݊ + ⏀) ܿଵ cos Ω݊ + ܿଶ sin(Ω݊)
Prof: Sarun Soman, MIT, Manipal 35
36. Example
Consider the RC ckt .Find a particular
solution for this system with an input
ݔ ݐ = cos(߱.)ݐ
Ans:
From previous example
ݕ ݐ + ܴܥ
ௗ
ௗ௧
ݕ ݐ = )ݐ(ݔ (1)
ݕ ݐ = ܿଵcos(⍵)ݐ + ܿଶ sin(⍵)ݐ
Substitute in (1)
ݕ ݐ + ܴܥ
݀
݀ݐ
ݕ
ݐ = cos(߱)ݐ
ܿଵ cos ⍵ݐ + ܿଶ sin(⍵)ݐ
− RC⍵ܿଵ sin ⍵ݐ
+ RC⍵ܿଶ cos ⍵ݐ
= cos(⍵)ݐ
Equating the coefficients of ܿଵand ܿଶ
ܿଵ + RC⍵ܿଶ = 1
−RC⍵ܿଵ + ܿଶ = 0
Solving for ܿଵand ܿଶ
ܿଵ =
1
1 + (RC⍵)ଶ
ܿଶ =
RC⍵
1 + (RC⍵)ଶ
Prof: Sarun Soman, MIT, Manipal 36
37. Example
ݕ ݐ =
1
1 + (RC⍵)ଶ
cos ⍵ݐ
+
RC⍵
1 + (RC⍵)ଶ
sin ⍵ݐ
Determine the particular solution for
the system described by the
following differential equations.
ݕ ݐ = ܿ
0 + 10ܿ = 4
ܿ =
2
5
ݕ ݐ =
2
5
b)ݔ ݐ = cos(3)ݐ
Ans:
ݕ ݐ = ܿଵcos(3)ݐ + ܿଶ sin(3)ݐ
݀
݀ݐ
ݕ
ݐ = −3ܿଵ sin(3)ݐ
+ 3 ܿଶcos(3)ݐ
−15ܿଵ sin 3ݐ + 15ܿଶ cos 3ݐ
+ 10ܿଵ cos 3ݐ
+ 10ܿଶ sin 3ݐ = 2 cos(3)ݐ
Equating coefficients
−15ܿଵ + 10ܿଶ = 0
10ܿଵ + 15ܿଶ = 2
Prof: Sarun Soman, MIT, Manipal 37
39. Solving Differential and Difference
Equations
The Complete Solution
Procedure for calculating complete solution
1. Find the form of the homogeneous solution ݕfrom the roots of the CE
equation.
2. Find a particular solution ݕby assuming that it is of the same form as
the input, yet is independent of all terms in the homogeneous solution.
3. Determine the coefficients in the homogeneous solution so that the
complete solution ݕ = ݕ
+ ݕ
satisfies the initial conditions.
Prof: Sarun Soman, MIT, Manipal 39
40. Example
Find the solution for the first order
recursive system described by the
difference equation.
ݕ ݊ −
1
4
ݕ ݊ − 1 = ]݊[ݔ
If the input ݔ ݊ = (
ଵ
ଶ
)]݊[ݑ and the
initial condition is ݕ −1 = 8.
Ans:
N=1
Homogenous solution
ݕ ݊ −
1
4
ݕ ݊ − 1 = 0
ݕ
݊ = ܿଵݎ
ݎ
−
1
4
ݎିଵ
= 0
ݎ −
1
4
= 0
ݕ ݊ = ܿଵ(
1
4
)
Particular solution
ݕ ݊ = ܿଶ(
1
2
)
ܿଶ(
1
2
)
−
1
4
ܿଶ(
1
2
)ିଵ
= (
1
2
)
ܿଶ −
1
2
ܿଶ = 1
ܿଶ = 2
Prof: Sarun Soman, MIT, Manipal 40
46. Characteristics of Systems Described by
Differential and Difference Equations
It is informative to express the o/p of a system as sum of two
components.
1) Only with initial conditions
2) Only with input
ݕ = ݕ + ݕ
Natural response ࢟ (zero i/p response)
System o/p for zero i/p
Describes the manner in which the system dissipates any stored
energy or memory.
Prof: Sarun Soman, MIT, Manipal 46
47. Characteristics of Systems Described by
Differential and Difference Equations
Procedure to calculate ݕ
1. Find the homogeneous solution.
2. From the homogeneous solution find the coefficients ܿ using initial
conditions.
Note: homogeneous solutions apply for all time, the natural
response is determined without translating initial conditions.
Eg.
A system is described by the difference equation
ݕ ݊ −
ଵ
ସ
ݕ ݊ − 1 = ݔ ݊ , ݕ −1 = 8. Find the natural response
of the system.
Prof: Sarun Soman, MIT, Manipal 47
48. Characteristics of Systems Described by
Differential and Difference Equations
Homogenous solution
ݕ
݊ = ܿଵ
1
4
Use initial conditions to find ܿଵ
Translation not required
ݕ −1 = ܿଵ
1
4
ିଵ
ܿଵ = 2
ݕ
= 2
1
4
, ݊ ≥ −1
Prof: Sarun Soman, MIT, Manipal 48
49. Characteristics of Systems Described by
Differential and Difference Equations
The Forced Response ݕ
System o/p due to the i/p signal assuming zero initial conditions.
The forced response is of the same form as the complete
solution.
Eg.
ݕ ݊ −
ଵ
ସ
ݕ ݊ − 1 = ݔ ݊ .Find the forced response of this
system if the i/p is ݔ ݊ =
ଵ
ଶ
]݊[ݑ
Ans:
Homogenous solution
Prof: Sarun Soman, MIT, Manipal 49