The transfer function of a control system is defined as the ratio of the Laplace transform of the output variable to the Laplace transform of the input variable. It represents the system in Laplace domain. To determine the transfer function, the system equations are transformed into Laplace domain and the ratio of the output and input Laplace transforms is taken. The transfer function is independent of initial conditions and inputs, and characterizes the system based on its parameters.
Mathematical Modelling of Control SystemsDivyanshu Rai
Different types of mathematical modeling in control systems [which include Mathematical Modeling of Mechanical and Electrical System (which further includes, Force-Voltage and Force-Current Analogies)]
Transient and Steady State Response - Control Systems EngineeringSiyum Tsega Balcha
. Two crucial aspects of this behavior are transient and steady-state responses. These concepts encapsulate how a system behaves over time, from the moment an input is applied to when the system settles into a stable state. The transient response of a system characterizes its behavior during the initial phase after a change in input. It reflects how the system reacts as it transitions from one state to another. This phase is marked by dynamic changes in the system's output as it adjusts to the new conditions imposed by the input.
Characteristics of Transient Response are Time Constant, overshoot, settling time and damping.
Once the transient effects have subsided, the system enters the steady-state, where its behavior becomes constant over time. In this phase, the system operates under stable conditions, and its output remains within a narrow range around the desired value, despite fluctuations in input or external disturbances. Characteristics of Steady-State Response are Steady-State Error, stability, accuracy, robustness,.
Mathematical Modelling of Control SystemsDivyanshu Rai
Different types of mathematical modeling in control systems [which include Mathematical Modeling of Mechanical and Electrical System (which further includes, Force-Voltage and Force-Current Analogies)]
Transient and Steady State Response - Control Systems EngineeringSiyum Tsega Balcha
. Two crucial aspects of this behavior are transient and steady-state responses. These concepts encapsulate how a system behaves over time, from the moment an input is applied to when the system settles into a stable state. The transient response of a system characterizes its behavior during the initial phase after a change in input. It reflects how the system reacts as it transitions from one state to another. This phase is marked by dynamic changes in the system's output as it adjusts to the new conditions imposed by the input.
Characteristics of Transient Response are Time Constant, overshoot, settling time and damping.
Once the transient effects have subsided, the system enters the steady-state, where its behavior becomes constant over time. In this phase, the system operates under stable conditions, and its output remains within a narrow range around the desired value, despite fluctuations in input or external disturbances. Characteristics of Steady-State Response are Steady-State Error, stability, accuracy, robustness,.
Ch2 mathematical modeling of control system Elaf A.Saeed
Chapter 2 Mathematical modeling of control system From the book (Ogata Modern Control Engineering 5th).
2-1 introduction.
2-2 transfer function and impulse response function.
2-3 automatic control systems.
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
Ch5 transient and steady state response analyses(control)Elaf A.Saeed
Chapter 5 Transient and steady-state response analyses. From the book (Ogata Modern Control Engineering 5th).
5-1 introduction.
5-2 First-Order System.
5-3 second-order system.
5-6 Routh’s stability criterion.
5-8 Steady-state errors in unity-feedback control systems.
Poles and Zeros of a transfer function are the frequencies for which the value of the denominator and numerator of transfer function becomes zero respectively
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/
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
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.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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.
2. Transfer function
The transfer function of a control system is defined as the ratio of the
Laplace transform of the output variable to Laplace transform of the
input variable assuming all initial conditions to be zero.
i.e. Transform of function =
𝐿 𝑇 𝑜𝑓 𝑂𝑢𝑡𝑝𝑢𝑡
𝐿 𝑇 𝑜𝑓 𝑖𝑛𝑝𝑢𝑡
3. Transfer function
• It is not necessary that output and input of a control system are of same
category.
• For example, in electric motors the input is electrical signal whereas the
output is mechanical signal since electrical energy required to rotate the
motors.
• But for mathematical analysis, of a system all kinds of signals should be
represented in a similar form.
• This is done by transforming all kinds of signal to their Laplace form.
• Also the transfer function of a system is represented by Laplace form by
dividing output Laplace transfer function to input Laplace transfer
function.
4.
5. Procedure for determining the transfer
function of a control system are as follows:
1) We form the equations for the system.
2) Now we take Laplace transform of the system equations, assuming
initial conditions as zero.
3) Specify system output and input.
4) Lastly we take the ratio of the Laplace transform of the output and the
Laplace transform of the input which is the required transfer function.
6. Example of L.T
G C (S)
R(S)
H
R(S)- H .C(S)
H.C(S)
[R(S)- H .C(S)] .G
8. Properties
1. The transfer function of a system is the Laplace transform of its impulse
response for zero initial conditions.
2. The transfer function can be determined from system input-output pair by
taking ratio Laplace of output to Laplace of input.
3. The transfer function is independent of the inputs to the system.
4. The system poles/zeros can be found out from transfer function.
5. The transfer function is defined only for linear time invariant systems. It is
not defined for non-linear system.
9. Advantages :-
1. It is a mathematical model that gives the gain of the given block/system.
2. Integral and differential equations are converted to simple algebraic
equations.
3. Once the transfer function is known, any output for any given input, can
be known.
4. System differential equation can be obtained by replacement of variable
‘s’ by ‘d/dt’
5. The value of transfer function is dependent on the parameters of the
system and independent of the input applied
10. Disadvantages :-
1) Transfer function is valid only for Linear Time Invariant
systems.
2) It does not take into account the initial conditions. Initial
conditions loose their significance.
3) It does not give any idea about how the present output is
progressing.