Part of Lecture Series on Automatic Control Systems delivered by me to Final year Diploma in Engg. Students. Easy language and Equally useful for higher level.
Part of Lecture Series on Automatic Control Systems delivered by me to Final year Diploma in Engg. Students. Easy language and Equally useful for higher level.
This seminar presentation summarizes the Routh stability criterion, which is used to investigate the stability of systems. The presentation defines stability, provides an example of an unstable system with the Tacoma Narrows bridge collapse, and explains how the Routh table is constructed from the coefficients of a system's characteristic equation. Special cases that can occur with the Routh table are described. As an example, the method is demonstrated on a closed-loop control system to determine stability. Practical applications include using the Routh criterion to find the range of controller gains that ensure a system remains stable.
1. The document discusses the Routh-Hurwitz stability criterion, which is a test used to determine the stability of linear time-invariant systems by constructing a Routh array from the coefficients of the characteristic equation and analyzing it.
2. Special cases that can occur with the Routh array, such as a zero in the first column or an entire zero row, are explained along with their implications.
3. An important application of the Routh-Hurwitz criterion is determining the range of values for a system gain K that ensures stability.
The document discusses stability analysis of linear time-invariant systems. It defines stability, instability, and marginal stability. A stable system is one where the natural response approaches zero over time. An unstable system's natural response grows without bound over time. A marginally stable system's natural response remains constant or oscillates over time.
The Routh-Hurwitz criterion is introduced as a method to determine stability without calculating system poles directly. It involves generating a Routh table from the characteristic equation and interpreting the table to determine the number of poles in each part of the s-plane - the left half-plane, right half-plane, or the jω-axis. If the Routh table has any sign changes in
This document discusses stability analysis of control systems using transfer functions and the Routh-Hurwitz criterion. It begins by defining stability and describing different types of system responses. The key points are:
1) The Routh-Hurwitz criterion can determine stability by analyzing the signs in the first column of a constructed Routh table, with changes in sign indicating right half-plane poles and instability.
2) Special cases like a zero only in the first column or an entire row of zeros require alternative methods like the epsilon method or reversing coefficients.
3) Examples demonstrate applying the Routh-Hurwitz criterion to determine stability for different polynomials, including handling special cases. Exercises also have readers practice stability analysis using
This document provides an overview of Routh's stability criterion, an algorithm for determining the stability of a closed-loop system based on the coefficients of its characteristic equation. The algorithm constructs an array based on the coefficients, and the number of sign changes in the first column indicates the number of roots with positive real parts. Special cases for zero elements are also described. Examples are provided to illustrate the algorithm and how it can determine stability as well as acceptable ranges for design parameters.
The document discusses the Routh-Hurwitz stability criterion for analyzing the stability of systems. It explains that the criterion uses the coefficients of the characteristic equation to form Hurwitz determinants. If all the determinants are positive, the system is stable. It also describes the Routh array method, where the signs of the first column determine stability - all positive signs means the system is stable. Special cases like rows of all zeros are also addressed.
Introduction, Types of Stable System, Routh-Hurwitz Stability Criterion, Disadvantages of Hurwitz Criterion, Techniques of Routh-Hurwitz criterion, Examples, Special Cases of Routh Array, Advantages and Disadvantages of Routh-Hurwitz Stability Criterion, and examples.
Part of Lecture Series on Automatic Control Systems delivered by me to Final year Diploma in Engg. Students. Easy language and Equally useful for higher level.
This seminar presentation summarizes the Routh stability criterion, which is used to investigate the stability of systems. The presentation defines stability, provides an example of an unstable system with the Tacoma Narrows bridge collapse, and explains how the Routh table is constructed from the coefficients of a system's characteristic equation. Special cases that can occur with the Routh table are described. As an example, the method is demonstrated on a closed-loop control system to determine stability. Practical applications include using the Routh criterion to find the range of controller gains that ensure a system remains stable.
1. The document discusses the Routh-Hurwitz stability criterion, which is a test used to determine the stability of linear time-invariant systems by constructing a Routh array from the coefficients of the characteristic equation and analyzing it.
2. Special cases that can occur with the Routh array, such as a zero in the first column or an entire zero row, are explained along with their implications.
3. An important application of the Routh-Hurwitz criterion is determining the range of values for a system gain K that ensures stability.
The document discusses stability analysis of linear time-invariant systems. It defines stability, instability, and marginal stability. A stable system is one where the natural response approaches zero over time. An unstable system's natural response grows without bound over time. A marginally stable system's natural response remains constant or oscillates over time.
The Routh-Hurwitz criterion is introduced as a method to determine stability without calculating system poles directly. It involves generating a Routh table from the characteristic equation and interpreting the table to determine the number of poles in each part of the s-plane - the left half-plane, right half-plane, or the jω-axis. If the Routh table has any sign changes in
This document discusses stability analysis of control systems using transfer functions and the Routh-Hurwitz criterion. It begins by defining stability and describing different types of system responses. The key points are:
1) The Routh-Hurwitz criterion can determine stability by analyzing the signs in the first column of a constructed Routh table, with changes in sign indicating right half-plane poles and instability.
2) Special cases like a zero only in the first column or an entire row of zeros require alternative methods like the epsilon method or reversing coefficients.
3) Examples demonstrate applying the Routh-Hurwitz criterion to determine stability for different polynomials, including handling special cases. Exercises also have readers practice stability analysis using
This document provides an overview of Routh's stability criterion, an algorithm for determining the stability of a closed-loop system based on the coefficients of its characteristic equation. The algorithm constructs an array based on the coefficients, and the number of sign changes in the first column indicates the number of roots with positive real parts. Special cases for zero elements are also described. Examples are provided to illustrate the algorithm and how it can determine stability as well as acceptable ranges for design parameters.
The document discusses the Routh-Hurwitz stability criterion for analyzing the stability of systems. It explains that the criterion uses the coefficients of the characteristic equation to form Hurwitz determinants. If all the determinants are positive, the system is stable. It also describes the Routh array method, where the signs of the first column determine stability - all positive signs means the system is stable. Special cases like rows of all zeros are also addressed.
Introduction, Types of Stable System, Routh-Hurwitz Stability Criterion, Disadvantages of Hurwitz Criterion, Techniques of Routh-Hurwitz criterion, Examples, Special Cases of Routh Array, Advantages and Disadvantages of Routh-Hurwitz Stability Criterion, and examples.
This presentation gives complete idea about definitions of stability, BIBO, Absolute and relative stability, Routh-Hurwitz Criterion, Special Cases and numerical examples.
Here is a quick review of the topic- Stability in Control System that might help you.
**A system is said to be stable, if its output is under control. Otherwise, it is said to be unstable**
In this presentation we have,
- Intro of Stability
-Types of System
- Concept of Stability
- Routh Hurwitz Criteria
- Limitations of Hurwitz Criterion
- Concluded
Hope this will be beneficial.
Thanking in anticipation.
The document discusses multiple topics related to analog control systems, including:
1. Reducing multiple subsystems into a single block to simplify analysis.
2. Describing system response in terms of transient and steady state response.
3. Explaining poles, zeros and how they relate to system response.
4. Defining characteristics of second order systems and analyzing steady state error.
5. Discussing stability analysis in the complex s-plane and conditions for stable, unstable and marginally stable systems.
This formula book gives simple and useful formulas related to control system. It helps students in solving numerical problems, in their competitive examinations
The Routh-Hurwitz stability criterion is a mathematical test that determines the stability of a linear time-invariant control system. It uses the characteristic polynomial of the system and constructs Hurwitz determinants to analyze the signs and determine if all roots of the polynomial have negative real parts, indicating stability. The document discusses the theorem, characteristic equation, determinant form, and references related to the Routh-Hurwitz stability criterion.
This chapter discusses transient response in control systems. It describes how to determine the time response from a transfer function using poles and zeros. For a first order system, the chapter defines the time constant, rise time and settling time. For a second order system, it defines damping ratio, percent overshoot, settling time and peak time. The chapter also discusses higher order systems and how to approximate them as second order systems. Exercises are provided to analyze systems and design feedback control systems based on desired transient response specifications.
This document outlines the steps and procedure for performing a root locus analysis. It begins with an introduction to root locus analysis and its use in determining the stability of a closed-loop system. It then lists the general 8 steps for drawing a root locus as a parameter is varied. An example problem is worked through, showing the determination of poles, zeros, asymptotes, breakaway points, and drawing the overall root locus sketch. The document concludes with a brief MATLAB program for generating a root locus plot.
Stability: Methods of determining stability, Routh Hurwitz Criterion, Root Locus, Frequency Domain Analysis: Resonant Peak, Resonant frequency and Bandwidth of the second order system, Effect of adding a zero and a pole to the forward path, Nyquist Stability Criterion, Relative Stability: Gain Margin and Phase Margin, Bode Plot.
This document discusses steady-state error analysis for unity feedback systems. It defines the position, velocity, and acceleration static error constants, which determine the steady-state error for step, ramp, and parabolic inputs respectively. The system type, defined by the number of integrations in the forward path (n=0 for Type 0, n=1 for Type 1, etc.), also determines the steady-state error formula. For a unit step input, the steady-state error equals the position constant divided by the input. For a unit ramp input, the steady-state error equals the velocity constant divided by the slope of the input ramp. For a parabolic input, the steady-state error equals the acceleration constant divided by
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
The document discusses several topics related to analog control systems including:
1. Reducing multiple subsystems into a single block to simplify analysis.
2. Describing system response in terms of transient and steady state response.
3. Explaining poles, zeros and how they impact system response.
4. Defining characteristics of second order systems and analyzing their step response.
5. Calculating steady state error for different input types.
6. Analyzing stability by examining the location of poles in the complex s-plane.
This document discusses using cascade compensation to improve control system performance. Cascade compensation involves adding additional poles and zeros to the open-loop transfer function. This can improve the transient response by placing poles farther out in the s-plane, and improve steady-state error by increasing the system type. An example shows designing a PI controller to reduce steady-state error to zero without affecting the 57.4% overshoot transient response. Pole-zero cancellation is used to maintain the original transient response while increasing the system type.
This document discusses stability analysis and design of control systems using the Routh-Hurwitz criteria. It provides definitions of stable, unstable, and marginally stable systems and describes how to generate and interpret the Routh table to determine the number and location of poles. The Routh-Hurwitz criteria allows determining stability based on the number of sign changes in the first column of the table and identifying marginally stable systems with poles only on the imaginary axis. Examples are provided to illustrate applying the method to determine stability and finding design parameters that achieve different stability conditions.
This document provides an overview of transfer functions and stability analysis of linear time-invariant (LTI) systems. It discusses how the Laplace transform can be used to represent signals as algebraic functions and calculate transfer functions as the ratio of the Laplace transforms of the output and input. Poles and zeros are introduced as important factors for stability. A system is stable if all its poles reside in the left half of the s-plane and unstable if any pole resides in the right half-plane. Examples are provided to demonstrate calculating transfer functions from differential equations and analyzing stability based on pole locations.
The document defines key terms used in signal flow graphs such as input/output nodes, forward/feedback paths, path/loop gains, and non-touching loops. It also provides Mason's rule for determining the transfer function of a system from its signal flow graph representation, which is defined as the ratio of the output to input with adjustments made for any feedback loops that touch the forward paths. Examples are also given of constructing signal flow graphs from block diagrams and applying Mason's rule to solve for various transfer functions.
This document discusses mathematical modeling for control systems engineering. It introduces mathematical models as simplified representations of physical systems using assumptions. There are two common types of mathematical models: transfer functions in the frequency domain and state equations in the time domain. The document outlines steps for creating mathematical models using laws of physics and engineering, and describes various modeling techniques including transfer functions, Laplace transforms, and partial fraction expansions. It emphasizes that mathematical modeling permits representing physical systems as separate entities that can be algebraically manipulated.
This document provides an overview of signal flow graphs and Mason's rule for calculating transfer functions from such graphs. It begins with definitions of key signal flow graph concepts like nodes, branches, paths, and loops. It then gives examples of constructing signal flow graphs from sets of simultaneous equations and converting block diagrams. Mason's rule is explained as providing the transfer function from a single formula involving the forward path gains and graph determinants, avoiding successive block diagram reductions. Finally, the document works through two examples applying Mason's rule to calculate transfer functions from given signal flow graphs.
Here are the steps to solve for the transfer function G(s) = X2(s)/F(s) for the given system:
1. Draw the free body diagrams for both masses M1 and M2 showing all the forces acting on each mass.
2. Write the Newton's second law equation for M1:
(M1s2 + f1vs + k1)X1(s) - k2(X1(s) - X2(s)) = 0
3. Write the Newton's second law equation for M2:
-k2(X1(s) - X2(s)) + (M2s2 + f2vs + k
This document provides an overview of analog control systems and Laplace transforms. It introduces key concepts like Laplace transforms, common time domain inputs, transfer functions, and modeling electrical, mechanical and electromechanical systems using block diagrams and mathematical models. Examples are provided to illustrate Laplace transforms, transfer functions, and analyzing system response using poles, zeros and stability analysis.
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.
Novel dependencies of currents and voltages in power system steady state mode...TELKOMNIKA JOURNAL
The unbalanced mode, negative/zero sequence, variation of real power are caused by the nonlinear or unbalanced loads increase the power transmission losses in distributing power systems and also harmful to the electric devices. Reactive power compensation is considered as the common methods for overcoming asymmetry. The critical issue in reactive power compensation is the optimal calculation of compensation values that is extremely difficult in complex circuits. We proposed a novel approach to overcome these difficulties by providing the creation of new analytical connections of the steady-state mode parameters (voltages, currents) depends on the controlled parameter for the arbitrary circuits. The base of our approach to reactive power compensation is the fractional-polynomial functions. We present a new description of the behavior of voltages and currents depending on the controlled parameters of the reactive power compensation devices, and we prove its effectiveness.
This presentation gives complete idea about definitions of stability, BIBO, Absolute and relative stability, Routh-Hurwitz Criterion, Special Cases and numerical examples.
Here is a quick review of the topic- Stability in Control System that might help you.
**A system is said to be stable, if its output is under control. Otherwise, it is said to be unstable**
In this presentation we have,
- Intro of Stability
-Types of System
- Concept of Stability
- Routh Hurwitz Criteria
- Limitations of Hurwitz Criterion
- Concluded
Hope this will be beneficial.
Thanking in anticipation.
The document discusses multiple topics related to analog control systems, including:
1. Reducing multiple subsystems into a single block to simplify analysis.
2. Describing system response in terms of transient and steady state response.
3. Explaining poles, zeros and how they relate to system response.
4. Defining characteristics of second order systems and analyzing steady state error.
5. Discussing stability analysis in the complex s-plane and conditions for stable, unstable and marginally stable systems.
This formula book gives simple and useful formulas related to control system. It helps students in solving numerical problems, in their competitive examinations
The Routh-Hurwitz stability criterion is a mathematical test that determines the stability of a linear time-invariant control system. It uses the characteristic polynomial of the system and constructs Hurwitz determinants to analyze the signs and determine if all roots of the polynomial have negative real parts, indicating stability. The document discusses the theorem, characteristic equation, determinant form, and references related to the Routh-Hurwitz stability criterion.
This chapter discusses transient response in control systems. It describes how to determine the time response from a transfer function using poles and zeros. For a first order system, the chapter defines the time constant, rise time and settling time. For a second order system, it defines damping ratio, percent overshoot, settling time and peak time. The chapter also discusses higher order systems and how to approximate them as second order systems. Exercises are provided to analyze systems and design feedback control systems based on desired transient response specifications.
This document outlines the steps and procedure for performing a root locus analysis. It begins with an introduction to root locus analysis and its use in determining the stability of a closed-loop system. It then lists the general 8 steps for drawing a root locus as a parameter is varied. An example problem is worked through, showing the determination of poles, zeros, asymptotes, breakaway points, and drawing the overall root locus sketch. The document concludes with a brief MATLAB program for generating a root locus plot.
Stability: Methods of determining stability, Routh Hurwitz Criterion, Root Locus, Frequency Domain Analysis: Resonant Peak, Resonant frequency and Bandwidth of the second order system, Effect of adding a zero and a pole to the forward path, Nyquist Stability Criterion, Relative Stability: Gain Margin and Phase Margin, Bode Plot.
This document discusses steady-state error analysis for unity feedback systems. It defines the position, velocity, and acceleration static error constants, which determine the steady-state error for step, ramp, and parabolic inputs respectively. The system type, defined by the number of integrations in the forward path (n=0 for Type 0, n=1 for Type 1, etc.), also determines the steady-state error formula. For a unit step input, the steady-state error equals the position constant divided by the input. For a unit ramp input, the steady-state error equals the velocity constant divided by the slope of the input ramp. For a parabolic input, the steady-state error equals the acceleration constant divided by
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
The document discusses several topics related to analog control systems including:
1. Reducing multiple subsystems into a single block to simplify analysis.
2. Describing system response in terms of transient and steady state response.
3. Explaining poles, zeros and how they impact system response.
4. Defining characteristics of second order systems and analyzing their step response.
5. Calculating steady state error for different input types.
6. Analyzing stability by examining the location of poles in the complex s-plane.
This document discusses using cascade compensation to improve control system performance. Cascade compensation involves adding additional poles and zeros to the open-loop transfer function. This can improve the transient response by placing poles farther out in the s-plane, and improve steady-state error by increasing the system type. An example shows designing a PI controller to reduce steady-state error to zero without affecting the 57.4% overshoot transient response. Pole-zero cancellation is used to maintain the original transient response while increasing the system type.
This document discusses stability analysis and design of control systems using the Routh-Hurwitz criteria. It provides definitions of stable, unstable, and marginally stable systems and describes how to generate and interpret the Routh table to determine the number and location of poles. The Routh-Hurwitz criteria allows determining stability based on the number of sign changes in the first column of the table and identifying marginally stable systems with poles only on the imaginary axis. Examples are provided to illustrate applying the method to determine stability and finding design parameters that achieve different stability conditions.
This document provides an overview of transfer functions and stability analysis of linear time-invariant (LTI) systems. It discusses how the Laplace transform can be used to represent signals as algebraic functions and calculate transfer functions as the ratio of the Laplace transforms of the output and input. Poles and zeros are introduced as important factors for stability. A system is stable if all its poles reside in the left half of the s-plane and unstable if any pole resides in the right half-plane. Examples are provided to demonstrate calculating transfer functions from differential equations and analyzing stability based on pole locations.
The document defines key terms used in signal flow graphs such as input/output nodes, forward/feedback paths, path/loop gains, and non-touching loops. It also provides Mason's rule for determining the transfer function of a system from its signal flow graph representation, which is defined as the ratio of the output to input with adjustments made for any feedback loops that touch the forward paths. Examples are also given of constructing signal flow graphs from block diagrams and applying Mason's rule to solve for various transfer functions.
This document discusses mathematical modeling for control systems engineering. It introduces mathematical models as simplified representations of physical systems using assumptions. There are two common types of mathematical models: transfer functions in the frequency domain and state equations in the time domain. The document outlines steps for creating mathematical models using laws of physics and engineering, and describes various modeling techniques including transfer functions, Laplace transforms, and partial fraction expansions. It emphasizes that mathematical modeling permits representing physical systems as separate entities that can be algebraically manipulated.
This document provides an overview of signal flow graphs and Mason's rule for calculating transfer functions from such graphs. It begins with definitions of key signal flow graph concepts like nodes, branches, paths, and loops. It then gives examples of constructing signal flow graphs from sets of simultaneous equations and converting block diagrams. Mason's rule is explained as providing the transfer function from a single formula involving the forward path gains and graph determinants, avoiding successive block diagram reductions. Finally, the document works through two examples applying Mason's rule to calculate transfer functions from given signal flow graphs.
Here are the steps to solve for the transfer function G(s) = X2(s)/F(s) for the given system:
1. Draw the free body diagrams for both masses M1 and M2 showing all the forces acting on each mass.
2. Write the Newton's second law equation for M1:
(M1s2 + f1vs + k1)X1(s) - k2(X1(s) - X2(s)) = 0
3. Write the Newton's second law equation for M2:
-k2(X1(s) - X2(s)) + (M2s2 + f2vs + k
This document provides an overview of analog control systems and Laplace transforms. It introduces key concepts like Laplace transforms, common time domain inputs, transfer functions, and modeling electrical, mechanical and electromechanical systems using block diagrams and mathematical models. Examples are provided to illustrate Laplace transforms, transfer functions, and analyzing system response using poles, zeros and stability analysis.
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.
Novel dependencies of currents and voltages in power system steady state mode...TELKOMNIKA JOURNAL
The unbalanced mode, negative/zero sequence, variation of real power are caused by the nonlinear or unbalanced loads increase the power transmission losses in distributing power systems and also harmful to the electric devices. Reactive power compensation is considered as the common methods for overcoming asymmetry. The critical issue in reactive power compensation is the optimal calculation of compensation values that is extremely difficult in complex circuits. We proposed a novel approach to overcome these difficulties by providing the creation of new analytical connections of the steady-state mode parameters (voltages, currents) depends on the controlled parameter for the arbitrary circuits. The base of our approach to reactive power compensation is the fractional-polynomial functions. We present a new description of the behavior of voltages and currents depending on the controlled parameters of the reactive power compensation devices, and we prove its effectiveness.
2012
University of Dayton
Electrical and computer department
Nonlinear control system
ABDELBASET ELHANGARI
1011647470
Final project: Designing of Nonlinear Controllers[ ]
[Type the abstract of the document here. The abstract is typically a short summary of the contents of the
document. Type the abstract of the document here. The abstract is typically a short summary of the
contents of the document.]
Contents
1. Experiment #1:.................................................................................................................................... 2
Part A Experiment #1 (Feedback linearization): ................................................................................... 2
Part B of Experiment #1(Sliding Mode Control): .................................................................................. 8
2. Experiment #2:.................................................................................................................................. 12
3. Experiment #3:.................................................................................................................................. 14
Figures:
Figure 1: The System Phase Portrait ............................................................................................................ 4
Figure 2: The System is Open loop Stable .................................................................................................... 5
Figure 3: Regulation of the System States by the Controller ........................................................................ 6
Figure 4: Failure of the Same Controller in the Regulation Process .............................................................. 7
Figure 5: Sliding Mode Control with the Alternative Manifold ................................................................... 10
Figure 6: Sliding Mode Control with the Given Manifold ........................................................................... 11
Figure 7: The Pendulum States Being Regulated near the Zero .................................................................. 13
Figure 8: The phase Portrait of the Open Loop .......................................................................................... 15
Figure 9: Back Stepping Controller Succession........................................................................................... 18
file:///C:/Users/abdu/Desktop/non_lin_proj%23_Toc343179239
file:///C:/Users/abdu/Desktop/non_lin_proj%23_Toc343179240
file:///C:/Users/abdu/Desktop/non_lin_proj%23_Toc343179241
file:///C:/Users/abdu/Desktop/non_lin_proj%23_Toc343179242
file:///C:/Users/abdu/Desktop/non_lin_proj%23_Toc343179243
file:///C:/Users/abdu/Desktop/non_lin_proj%23_Toc343179244
file:///C:/Users/abdu/Desktop/non_lin_proj%23_Toc343179246
1. Experiment #1:
The system is given by
̇
̇
With (.
This document provides an overview of a control systems engineering course. It discusses key topics that will be covered, including modeling dynamic systems using differential equations, obtaining accurate plant models, designing controllers to meet performance specifications, handling uncertainties, and bridging the gap between linear control theory and real-world industrial problems. The course aims to address issues like robust stability, robust performance, nonlinearities, and designing controllers when plant models are difficult to stabilize.
This document discusses Routh's stability criterion for determining the stability of closed-loop control systems using block diagram reduction and analysis. It begins with an introduction to block diagrams and their components. It then covers block diagram reduction rules and examples. The main content discusses Routh's stability criterion algorithm and how to apply it to determine the number of roots with nonnegative real parts. It provides examples of applying the criterion to polynomials of different orders. It also discusses special cases when elements in the Routh array are zero and how to handle those cases. Finally, it discusses how Routh's criterion can be used to determine acceptable gain values and for relative stability analysis.
Finite Volume Method Advanced Numerical Analysis by Md.Al-AminMd. Al-Amin
Finite volume refers to the small volume surrounding each node point on a mesh. The finite volume method (FVM) is a numerical method that converts partial differential equations into algebraic equations. FVM involves (1) grid generation by dividing the domain into control volumes, (2) discretizing the governing equations by integrating over each control volume, and (3) solving the algebraic equations. FVM follows the conservation law by ensuring flux entering and leaving each control volume is identical. The document provides an example of using FVM to solve the 1D steady state heat equation.
Robust Fuzzy Output Feedback Controller for Affine Nonlinear Systems via T–S ...Mostafa Shokrian Zeini
This presentation concerns the design of a robust H_∞ fuzzy output feedback controller for a class of affine nonlinear systems with disturbance via Takagi-Sugeno (T–S) fuzzy bilinear model. The parallel distributed compensation (PDC) technique is utilized to design a fuzzy controller. The stability conditions of the overall closed loop T-S fuzzy bilinear model are formulated in terms of Lyapunov function via linear matrix inequality (LMI). The control law is robustified by H_∞ sense to attenuate external disturbance. Moreover, the desired controller gains can be obtained by solving a set of LMI.
Episode 50 : Simulation Problem Solution Approaches Convergence Techniques S...SAJJAD KHUDHUR ABBAS
Episode 50 : Simulation Problem Solution Approaches Convergence Techniques Simulation Strategies
3.2.3.3. Quasi-Newton (QN) Methods
These methods represent a very important class of techniques because of their extensive use in practical alqorithms. They attempt to use an approximation to the Jacobian and then update this at each step thus reducing the overall computational work.
The QN method uses an approximation Hk to the true Jacobian i and computes the step via a Newton-like iteration. That is,
SAJJAD KHUDHUR ABBAS
Ceo , Founder & Head of SHacademy
Chemical Engineering , Al-Muthanna University, Iraq
Oil & Gas Safety and Health Professional – OSHACADEMY
Trainer of Trainers (TOT) - Canadian Center of Human
Development
Data Approximation in Mathematical Modelling Regression Analysis and curve fi...Dr.Summiya Parveen
The document summarizes a lecture on regression analysis and curve fitting in mathematical modelling. It introduces regression analysis and its applications. It describes different regression techniques like linear, nonlinear, polynomial and multiple regression. It provides examples of fitting linear and polynomial curves to data using MATLAB. It discusses assessing the goodness of fit using metrics like residual norm and coefficient of determination.
Data Approximation in Mathematical Modelling Regression Analysis and Curve Fi...Dr.Summiya Parveen
Outline of the lecture:
Introduction of Regression
Application of Regression
Regression Techniques
Types of Regression
Goodness of fit
MATLAB/MATHEMATICA implementation with some example
Regression analysis is a form of predictive modelling technique which investigates the relationship between a dependent (target) and independent variable (s) (predictor). This technique is used for forecasting, time series modelling and finding the casual effect relationship between the variables. Regression analysis is an important tool for modelling and analysing data. Here, we fit a curve / line to the data points in such a manner that the differences between the distances of data points from the curve or line is minimized.
By DR. SUMMIYA PARVEEN
The document presents a numerical method for solving a continuous model of the economy expressed as a second-order nonlinear ordinary differential equation (ODE). A new approach called the Modified Taylor Series Approach (MTSA) is used to derive a two-step block method for directly solving the model ODE without first reducing it to a system of first-order equations. The MTSA allows the derivation of the integration coefficients to obtain the block method schemes for solving the ODE at multiple grid points simultaneously. The resulting MTSA-derived two-step block method is then applied to solve the specific second-order nonlinear continuous model of the economy under consideration.
A simplified method of designing a phase lead compensator to improve the m-s-...eSAT Journals
1. The document describes a method for designing a phase-lead compensator to improve the phase margin of a mass-spring-damper system.
2. The steps taken are to determine the current phase margin, calculate the difference to the desired margin, design the compensator transfer function, and verify that the compensated system meets requirements.
3. Using this method, a phase-lead compensator with a transfer function of 1+0.42S/(1+0.14S) is designed, and the compensated system is verified to have a phase margin of 45 degrees as required.
A simplified method of designing a phase lead compensator to improve the m-s-...eSAT Journals
Abstract In this study, based on the unconstrained third order shear deformation theory (UTSDT), numerical analysis of an axisymmetric bending and stresses of circular plate are investigated. The material properties are considerd to graded through the thickness of the verticlecoordinate, and follow a simple power of volume fraction of the constituents.governing equations are derived and DQM is used as an efficient numerical method for solving the differential equations.Two types of boundary conditions under the influence of the bending and body force are studied. The validation of the results is done by a comparison with another study ,which available in the literature and found good agreement between two studies. Index Terms:bending,shearstress,circularplate,UTSDT,GDQM.
Part of Lecture Series on Automatic Control Systems delivered by me to Final year Diploma in Engg. Students. Equally useful for higher level. Easy language and step by step procedure for drawing Bode Plots. Three illustrative examples are included.
Linear Control Hard-Disk Read/Write Controller AssignmentIsham Rashik
Classic Hard-Disk Read/Write Head Controller Assignment completed using MATLAB and SIMULINK. To see the diagrams in detail, please download first and zoom it.
This document summarizes a mechanical engineering student project analyzing the kinematics and dynamics of a forging manipulator. It includes:
1) Modeling the hydraulic actuators as spring-damper systems and computing kinematics using vector equations of the degrees of freedom.
2) Computing static preloads on the actuators to achieve equilibrium.
3) Linearizing the equations of motion around the equilibrium position to determine natural frequencies and mode shapes.
4) Calculating frequency response functions by solving the linearized equations with an external forcing function.
ACTIVE CONTROLLER DESIGN FOR THE OUTPUT REGULATION OF THE WANG-CHEN-YUAN SYSTEMecij
In this paper, we design an active controller for regulating the output of the Wang-Chen-Yuan system (2009), which is one of the recently discovered 3-D chaotic systems. For the constant tracking problem, new state feedback control laws have been derived for regulating the output of the Wang-Chen-Yuan system. Numerical simulations using MATLAB are exhibited to validate and demonstrate the usefulness of the active controller design for the output regulation of the Wang-Chen-Yuan system.
This document describes a suspension system model for a heavy vehicle and the design of an active suspension controller. The model includes two masses connected by a spring and damper to represent the vehicle body and wheel. Transfer functions are derived relating the output displacement to control input and road disturbance. Simulation in MATLAB shows the open-loop system has an unacceptable overshoot and settling time. A PID controller is designed and tested, showing improved but still not ideal performance. Gains are increased further resulting in a system meeting requirements of less than 5% overshoot and 5 second settling time.
STABILITY ANALYSIS AND CONTROL OF A 3-D AUTONOMOUS AI-YUAN-ZHI-HAO HYPERCHAOT...ijscai
This document summarizes a research paper that analyzes the stability and control of a 3D autonomous hyperchaotic system using fuzzy logic modeling. The paper presents the equations that define the hyperchaotic Ai-Yuan-Zhi-Hao system. A Takagi-Sugeno fuzzy logic controller is designed to control the system. Lyapunov stability analysis is used to derive control laws for each fuzzy subsystem and conditions for asymptotic stability. Simulation results show the controlled trajectories converge when initial conditions are between -50 and 50, demonstrating controllability of the hyperchaotic attractor.
This document describes a simulation tool to model heat transfer through multiple materials. The tool calculates the temperature over time at the interface of each material when the bottom surface is subjected to a temperature curve. The model uses a finite difference approach and implicit time-stepping to solve the heat transfer equations. The equations are solved using both direct matrix inversion and an iterative Gauss-Seidel method, and both solutions are found to match.
- The document discusses alternating current (AC) and its generation using a simple AC generator. AC voltage and current change polarity and magnitude at regular time intervals.
- Key terms related to AC quantities are defined, including cycle, time period, frequency, peak value, average value, and root mean square (RMS) value. The frequency of power supply in India is 50 Hz.
- Behavior of AC circuits containing resistance, inductance, and capacitance is examined. Current and voltage are in phase for resistance. For inductance, current lags voltage by 90 degrees, while for capacitance current leads voltage by 90 degrees.
Segmental displays are electronic display devices that display alphanumeric characters and numbers using seven LED or LCD segments arranged in a rectangular form. The seven segments are named a to g. 7-segment displays are commonly used in digital clocks and meters. Dot matrix displays use a matrix of LED indicators arranged in a rectangular grid that can be independently turned on and off to display text, numbers or graphics. A 5x7 matrix is most commonly used, with 35 LEDs arranged in 7 rows and 5 columns, allowing individual LEDs to be addressed to display characters.
Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
Software Engineering and Project Management - Software Testing + Agile Method...Prakhyath Rai
Software Testing: A Strategic Approach to Software Testing, Strategic Issues, Test Strategies for Conventional Software, Test Strategies for Object -Oriented Software, Validation Testing, System Testing, The Art of Debugging.
Agile Methodology: Before Agile – Waterfall, Agile Development.
Generative AI Use cases applications solutions and implementation.pdfmahaffeycheryld
Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
https://www.leewayhertz.com/generative-ai-use-cases-and-applications/
Mechatronics is a multidisciplinary field that refers to the skill sets needed in the contemporary, advanced automated manufacturing industry. At the intersection of mechanics, electronics, and computing, mechatronics specialists create simpler, smarter systems. Mechatronics is an essential foundation for the expected growth in automation and manufacturing.
Mechatronics deals with robotics, control systems, and electro-mechanical systems.
Height and depth gauge linear metrology.pdfq30122000
Height gauges may also be used to measure the height of an object by using the underside of the scriber as the datum. The datum may be permanently fixed or the height gauge may have provision to adjust the scale, this is done by sliding the scale vertically along the body of the height gauge by turning a fine feed screw at the top of the gauge; then with the scriber set to the same level as the base, the scale can be matched to it. This adjustment allows different scribers or probes to be used, as well as adjusting for any errors in a damaged or resharpened probe.
Accident detection system project report.pdfKamal Acharya
The Rapid growth of technology and infrastructure has made our lives easier. The
advent of technology has also increased the traffic hazards and the road accidents take place
frequently which causes huge loss of life and property because of the poor emergency facilities.
Many lives could have been saved if emergency service could get accident information and
reach in time. Our project will provide an optimum solution to this draw back. A piezo electric
sensor can be used as a crash or rollover detector of the vehicle during and after a crash. With
signals from a piezo electric sensor, a severe accident can be recognized. According to this
project when a vehicle meets with an accident immediately piezo electric sensor will detect the
signal or if a car rolls over. Then with the help of GSM module and GPS module, the location
will be sent to the emergency contact. Then after conforming the location necessary action will
be taken. If the person meets with a small accident or if there is no serious threat to anyone’s
life, then the alert message can be terminated by the driver by a switch provided in order to
avoid wasting the valuable time of the medical rescue team.
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
1. BEE-502
Automatic Control Systems
Unit-3, Routh-Hurwitz (Contd ...)
Diploma in Engg.
(Electrical/ Instr. & Control)
5th Semester
Instructor: Mohd. Umar Rehman
16. 10. 2017
Page 1 of 3
Routh-Hurwitz Stability Criterion (Contd ...)
Problem: s ss s s+ + + + + =5 4 3 2
2 4 8 3 1 0 was the given characteristic equation. We
had already applied the epsilon-limit method. Now, we continue to solve the same
problem by replacing s by 1/z. Then, the characteristic equation becomes
z z z z
z z z z
z
z
+ + + + + =
+ + + + + =
5 4 3 2
5 4 3 2
1 1 1 1
2 4 8 3 1 0
3 8 4 2 1
1
0
Again form the Routh table as follows:
z 5
1 8 2
z 4
3 4 1
z 3
20/3 5/3 0
z 2
65/20 1 0
z 1
−0.38 0 0
z 0
1 0 0
Hence, there are two sign changes, meaning that the system is unstable. In final
exam, you are free to choose any method, but if any method is explicitly mentioned,
then use that method.
Special Case 2: Row of zeroes
Special case 1 was when only one element in the first column of Routh table is zero.
In special case 2, it may also happen that entire row has all zero elements. In such
cases, an auxiliary equation (AE) is formed using the coefficients of row just above
the zero row. Then, this equation is differentiated with respect to s and the row is
constructed using the coefficients of this differentiated equation. And then the usual
process of Routh-Hurwitz stability method is continued. Such a case is illustrated in
the following problem.
Problem: Determine the stability of a system whose characteristic equation is given
by: s s ss s+ + + + + =5 4 3 2
2 6 12 8 16 0
Solution: Form the Routh table as follows:
s 5
1 6 8
s 4
2 12 16
s 3
0 0 0
s 2
s 1
s 0
2. BEE-502
Automatic Control Systems
Unit-3, Routh-Hurwitz (Contd ...)
Diploma in Engg.
(Electrical/ Instr. & Control)
5th Semester
Instructor: Mohd. Umar Rehman
16. 10. 2017
Page 2 of 3
Hence, all the elements of the third row are zero. Now form the auxiliary polynomial
equation using the row just above the zero row as follows:
( )A s ss= + +4 2
6 8
Differentiating w. r. t. s
( )
d
A s s
ds
s= +3
124
Now, replace the zeroes in the third row by the coefficients of above differentiated
equation. Hence, the Routh table modifies to:
s 5
1 6 8
s 4
2 12 16
s 3
4 12 0
s 2
6 16
s 1
1/3 0
s 0
8
There are no sign changes, therefore the system is stable.
Applications of Routh-Hurwitz Criterion
Routh-Hurwitz criterion can be used to determine the range of values of some system
parameter for stability. Let us see one related problem.
Problem: Determine the range of K for stability of a unity feedback control system
whose open-loop transfer function is
( )
( )( )
G s
s s
K
s
=
+ +1 2
Solution: For a unity feedback system, H(s) = 1, the characteristic equation is
given by:
( ) ( )
( )
( )( )
G s H s
G s
K
s s s
s s s K
+ =
+ =
+ =
+ +
+ + =+3 2
1 0
1 0
1
2
2
3
0
1
0
Now, we form the Routh table as follows:
3. BEE-502
Automatic Control Systems
Unit-3, Routh-Hurwitz (Contd ...)
Diploma in Engg.
(Electrical/ Instr. & Control)
5th Semester
Instructor: Mohd. Umar Rehman
16. 10. 2017
Page 3 of 3
s 3
1 2
s 2
3 K
s 1
(6−K)/3
s 0
K
For stability, all the elements in the first column should be positive (no sign change
should be there). So, K > 0 and (6−K)/3 > 0.
⇒K > 0 and K < 6 ⇒ 0 < K < 6. If K = 6, then the system will be marginally
stable and exhibit sustained oscillations.
Assignment Problems
1. For a unity feedback system with the forward transfer function
( )
( )
( )( )
K s
G s
s s s
+
=
+ +
20
2 3
find the range of K to make the system stable.
2. Determine whether the unity feedback system shown in the following figure is
stable
where, ( )
( )( )( )( )
G s
s s s s
=
+ + + +
240
1 2 3 4
*** UNIT-3 ENDS ***