This presentation explains about the introduction of Nyquist Stability criterion. It clearly shows advantages and disadvantages of Nyquist Stability criterion and also explains importance of Nyquist Stability criterion and steps required to sketch the Nyquist plot. It explains about the steps required to sketch Nyquist plot clearly. It also explains about sketching Nyquist plot and determines the stability by using Nyquist Stability criterion with an example.
Chapter 7 Controls Systems Analysis and Design by the frequency response analysis . From the book (Ogata Modern Control Engineering 5th).
7-1 introduction.
7-2 Bode diagrams.
This presentation explains about the introduction of Nyquist Stability criterion. It clearly shows advantages and disadvantages of Nyquist Stability criterion and also explains importance of Nyquist Stability criterion and steps required to sketch the Nyquist plot. It explains about the steps required to sketch Nyquist plot clearly. It also explains about sketching Nyquist plot and determines the stability by using Nyquist Stability criterion with an example.
Chapter 7 Controls Systems Analysis and Design by the frequency response analysis . From the book (Ogata Modern Control Engineering 5th).
7-1 introduction.
7-2 Bode diagrams.
This Presentation explains about the introduction of Frequency Response Analysis. This video clearly shows advantages and disadvantages of Frequency Response Analysis and also explains frequency domain specifications and derivations of Resonant Peak, Resonant Frequency and Bandwidth.
Transfer Function and Mathematical Modeling
Transfer Function
Poles And Zeros of a Transfer Function
Properties of Transfer Function
Advantages and Disadvantages of T.F.
Translation motion
Rotational motion
Translation-Rotation counterparts
Analogy system
Force-Voltage analogy
Force-Current Analogy
Advantages
Example
Root locus is a graphical representation of the closed-loop poles as a system parameter is varied.
It can be used to describe qualitatively the performance of a system as various parameters are changed.
It gives graphic representation of a system’s transient response and also stability.
We can see the range of stability, instability, and the conditions that cause a system to break into oscillation.
time domain analysis, Rise Time, Delay time, Damping Ratio, Overshoot, Settli...Waqas Afzal
Time Response- Transient, Steady State
Standard Test Signals- U(t), S(t), R(t)
Analysis of First order system - for Step input
Analysis of second order system -for Step input
Time Response Specifications- Rise Time, Delay time, Damping Ratio, Overshoot, Settling Time
Calculations
CONTROL SYSTEMS PPT ON A LEAD COMPENSATOR CHARACTERISTICS USING BODE DIAGRAM ...sanjay kumar pediredla
A LEAD COMPENSATOR CHARACTERISTICS USING BODE DIAGRAM FOR MAXIMUM OF 50 DEG PHASE ANGLE
THIS PPT IS SO USEFUL FOR THE ENGINEERING STUDENTS FOR CONTROL SYSTEMS STUDENTS AND THIS PPT ALSO CONTAINS A MATLAB CODING FOR THE LEAD COMPENSATOR AND THE RESULTS ARE ALSO PLOTTED IN THAT PPT AND THE PROBLEM CAN ALSO BE SOLVED BY USING THE DATA IN PPT AND IT IS SO USEFUL PPT
this is presentation about time response analysis in control engineering. this is presentation on its types and many more like time responses with best example
This Presentation explains about the introduction of Frequency Response Analysis. This video clearly shows advantages and disadvantages of Frequency Response Analysis and also explains frequency domain specifications and derivations of Resonant Peak, Resonant Frequency and Bandwidth.
Transfer Function and Mathematical Modeling
Transfer Function
Poles And Zeros of a Transfer Function
Properties of Transfer Function
Advantages and Disadvantages of T.F.
Translation motion
Rotational motion
Translation-Rotation counterparts
Analogy system
Force-Voltage analogy
Force-Current Analogy
Advantages
Example
Root locus is a graphical representation of the closed-loop poles as a system parameter is varied.
It can be used to describe qualitatively the performance of a system as various parameters are changed.
It gives graphic representation of a system’s transient response and also stability.
We can see the range of stability, instability, and the conditions that cause a system to break into oscillation.
time domain analysis, Rise Time, Delay time, Damping Ratio, Overshoot, Settli...Waqas Afzal
Time Response- Transient, Steady State
Standard Test Signals- U(t), S(t), R(t)
Analysis of First order system - for Step input
Analysis of second order system -for Step input
Time Response Specifications- Rise Time, Delay time, Damping Ratio, Overshoot, Settling Time
Calculations
CONTROL SYSTEMS PPT ON A LEAD COMPENSATOR CHARACTERISTICS USING BODE DIAGRAM ...sanjay kumar pediredla
A LEAD COMPENSATOR CHARACTERISTICS USING BODE DIAGRAM FOR MAXIMUM OF 50 DEG PHASE ANGLE
THIS PPT IS SO USEFUL FOR THE ENGINEERING STUDENTS FOR CONTROL SYSTEMS STUDENTS AND THIS PPT ALSO CONTAINS A MATLAB CODING FOR THE LEAD COMPENSATOR AND THE RESULTS ARE ALSO PLOTTED IN THAT PPT AND THE PROBLEM CAN ALSO BE SOLVED BY USING THE DATA IN PPT AND IT IS SO USEFUL PPT
this is presentation about time response analysis in control engineering. this is presentation on its types and many more like time responses with best example
The Controller Design For Linear System: A State Space ApproachYang Hong
The controllers have been widely used in many industrial processes. The goal of accomplishing a practical control system design is to meet the functional requirements and achieve a satisfactory system performance. We will introduce the design method of the state feedback controller, the state observer and the servo controller with optimal control law for a linear system in this paper.
I am Martina J. I am a Signals and Systems Assignment Expert at matlabassignmentexperts.com. I hold a Master's in Matlab, from the University of Maryland. I have been helping students with their assignments for the past 9 years. I solve assignments related to Signals and Systems.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com.
You can also call on +1 678 648 4277 for any assistance with Signals and Systems assignments.
Time response of continuous data systems, Different test Signals for the time response, Unit step response and Time-Domain Specifications, Time response of a first-order and second order systems for different test signals, Steady State Error and Error constants, Sensitivity, Control Actions: Proportional, Derivative, Integral and PID control. Introduction to Process Control Systems, Pneumatic hydraulics, Actuators.
Giving description about time response, what are the inputs supplied to system, steady state response, effect of input on steady state error, Effect of Open Loop Transfer Function on Steady State Error, type 0,1 & 2 system subjected to step, ramp & parabolic input, transient response, analysis of first and second order system and transient response specifications
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
2. Objectives
To
find the steady-state error for a unity
feedback system
To specify a system’s steady-state error
performance
To design system parameters to meet
steady-state error performance
specifications
3. Introduction
In chapter 1, we learnt about 3
requirements needed when designing a
control system
Transient response
Stability
Steady-state errors (SSE)
Up until now we only covered until
transient response and stability
4. Review on transient response
We learned in Chapter 4, there are 4 types
of transient response for a second-order
system.
Overdamped
Underdamped
Undamped
Critically damped
5. Review on transient response
An example of elevator response
The transient response for elevator can be
considered as overdamped. The system is
stable but has steady-state error
6. Introduction
What is steady-state error?
Steady-state error is the difference between
the input and output for a certain test input as
t →∞
Test input used for steady-state error analysis
and design are
Step
Ramp
Parabola
8. Introduction
Example of systems tested using the test
signal.
Targeting system:
Targeting a static target. (e.g. a stopping car). We
test the system using step input because the position
of the car is in constant position.
Targeting a car moving with constant velocity. We
test the system using ramp input because the car is
moving in constant velocity.
Targeting an accelerating car. We test the system
using parabola input because the car is accelerating.
9. Introduction
We are only concerned with the difference
between the input and the output of a
feedback control system after the steady
state has been reached, our discussion is
limited to stable systems where the
natural response approaches zero when
(time) t approaches infinity.
10. SSE for unity feedback system
Unity feedback system can be represented
as
Steady state error can be calculated from
a system’s closed-loop transfer function,
T(s), or the open-loop transfer function,
G(s), for unity feedback systems.
11. SSE for unity feedback system
Closed loop transfer function, T(s) is calculated
by solving the unity feedback system using the
block diagram reduction method for feedback
system.
1
T ( s) =
G ( s)
1 + G ( s ) × (1)
12. SSE for unity feedback system
Open-loop transfer function for a unity
feedback system is the value of G(s) multiply
1.
1
1
13. SSE for unity feedback system
Steady state error in terms of T(s).
To find E(s), the error between the input, R(s) and
output, C(s), we write
E ( s) = R(s) − C ( s)
= R ( s ) − R ( s )T ( s )
= R ( s ) 1 − T ( s )
)
We can find final value of the error, e(∞ in terms of
T(s) using
e ( ∞ ) = lim sR( s ) 1 − T ( s )
s →0
We can only use this equation if T(s) is stable, E(s) has no
poles in the right-half plane or poles on the imaginary axis
other than the origin
14. SSE for unity feedback system
Example 7.1
Find the steady state-error for a unity feedback
system that has T(s) = 5/(s2+7s+10) and the
input is a unit step.
Solution:
R(s) =unit step = 1/s
T(s) = 5/(s2+7s+10), we must check the
stability of T(s) using Routh table or poles.
15. SSE for unity feedback system
Example 7.1 (cont.)
We know from the unity feedback system
E ( s) = R(s) − C (s )
C ( s) = R( s )T ( s )
So, E(s) can be calculated using both equation
E ( s) = R(s) − C (s)
= R ( s ) − R ( s )T ( s )
= R ( s ) 1 − T ( s )
16. SSE for unity feedback system
Example 7.1 (cont.)
E(s) in example 7.1 is
1
5
E ( s ) = 1 − 2
s s + 7 s + 10
1 s 2 + 7 s + 10
5
= 2
− 2
s s + 7 s + 10 s + 7 s + 10
2
s2 + 7s + 5
1
s + 7s + 5
= 2
=
s s + 7 s + 10 s s 2 + 7 s + 10
(
)
17. SSE for unity feedback system
Example 7.1 (cont.)
Before calculating the final value of the error
we must check the position of E(s) poles
s2 + 7s + 5
s 2 + 7s + 5
E ( s) =
=
2
s ( s + 2 ) ( s + 5)
s s + 7 s + 10
(
)
The poles for E(s) are at (0,0), (-2,0) and
(-5,0). Since all the poles are not on the right
half plane or the imaginary axis we can use the
equation to calculate final error value in terms
of T(s).
18. SSE for unity feedback system
Example 7.1 (cont.)
e ( ∞ ) = lim sR ( s ) 1 − T ( s )
s →0
5
1
= lim s ÷1 − 2
s →0
s s + 7 s + 10
5 5 1
= 1 − =
=
10 10 2
19. SSE for unity feedback system
Steady state error in terms of G(s)
We can find final value of the error, e(∞
)
in
terms of G(s) using
sR ( s )
e ( ∞ ) = lim
s →0 1 + G ( s )
We are going to use three types of input R(s);
step, ramp and parabola. So the final value of
the error for this types of input can be
described as
20. SSE for unity feedback system
Step input e(∞
)
e ( ∞ ) = estep ( ∞ ) =
1
sR(s)
1 + lim G ( s )
s →0
)
Ramp input e(∞
1
sR(s)
e ( ∞ ) = eramp ( ∞ ) =
lim sG ( s )
s →0
)
Parabola input e(∞
1
sR(s)
e ( ∞ ) = e parabola ( ∞ ) =
lim s 2G ( s )
s →0
21. SSE for unity feedback system
Steady state error with no integration
Example 7.2
Find the steady-state errors for inputs of 5u(t),
5tu(t), and 5t2u(t) to the system below.
No integration
Solution hint
5u(t) = unit step = 5(1/s)
5tu(t) = ramp = 5(1/s2)
5t2u(t) = parabola = 5(2/s3) = 10(1/s3)
23. SSE for unity feedback system
Example 7.2 (cont)
5
5
5
e ( ∞ ) = estep ( ∞ ) =
=
=
1 + lim G ( s ) 1 + 20 21
s →0
5
5
e ( ∞ ) = eramp ( ∞ ) =
= =∞
lim sG ( s ) 0
s →0
10
5
e ( ∞ ) = e parabola ( ∞ ) =
= =∞
2
lim s G ( s ) 0
s →0
24. SSE for unity feedback system
Try to solve steady state errors for
systems with one integration in Example
7.3.
25. SSE for unity feedback system
From the previous slides, the final error
value for three kinds of input; step, ramp
and parabola, are as follows
1
e ( ∞ ) = estep ( ∞ ) =
1 + lim G ( s )
position constant, K p
1
e ( ∞ ) = eramp ( ∞ ) =
lim sG ( s )
velocity constant, K v
s →0
s →0
1
e ( ∞ ) = eramp ( ∞ ) =
lim s 2G ( s )
s →0
acceleration constant, K a
26. SSE for unity feedback system
Steady state error via static error
constants
Example 7.4 (Figure 7.7 (a) )
27. SSE for unity feedback system
Solution
First step is to calculate the static error constants.
500( s + 2)( s + 5)( s + 6) 500(0 + 2)(0 + 5)(0 + 6)
K p = lim G ( s ) = lim
=
= 5.208
s →0
s →0 ( s + 8)( s + 10)( s + 12)
s (0 + 8)(0 + 10)(0 + 12)
s (500)( s + 2)( s + 5)( s + 6)
K v = lim sG ( s ) = lim
=0
s →0
s →0
( s + 8)( s + 10)( s + 12)
s 2 (500)( s + 2)( s + 5)( s + 6)
K a = lim s G ( s ) = lim
=0
s →0
s →0
( s + 8)( s + 10)( s + 12)
2
28. SSE for unity feedback system
Next step is to calculate the final error value.
1
Step input, e(∞) =
= 0.161
1+ K p
1
Ramp input, e(∞) =
=∞
Kv
1
Parabola input,e(∞) =
=∞
Ka
Try to solve the remaining problems in Figure
7.7 (a) and (c).
29. SSE for unity feedback system
System Type
We are still focusing on unity negative
feedback system.
Since steady-state errors are dependent upon
the number of integrations in the forward path,
we give a name to this system attribute.
30. SSE for unity feedback system
Below is a feedback control system for
defining system type.
We define the system type to be the value of n
in the denominator.
Type 0 when n = 0
Type 1 when n = 1
Type 2 when n = 2
31. SSE for unity feedback system
Relationship between input, system type,
static error constant, and steady-state
errors can be summarized as
32. SSE for unity feedback system
Steady-state error specifications.
We can use the static error constants to
represent the steady-state error characteristic
of our system.
Conclusion that we can made based on static
error constants.
Problem: What information is contained in the
specification Kv = 1000.
33. SSE for unity feedback system
Kv = 1000
Solution:
1. The system is stable.
2. The system is of Type 1, since only Type 1
have Kv that are finite constant
34. SSE for unity feedback system
3.
A ramp input is the test signal. Refer to table.
4.
The steady-state error between the input
ramp and the output ramp is 1/Kv per unit of
slope.