Modern Control - Lec 02 - Mathematical Modeling of SystemsAmr E. Mohamed
This document provides an overview of mathematical modeling of physical systems. It discusses how to derive mathematical models from physical systems using differential equations based on governing physical laws. The key steps are: (1) defining the physical system, (2) formulating the mathematical model using differential equations, and (3) solving the equations. Common model types include differential equation, transfer function, and state-space models. The document also discusses modeling various physical elements like electrical circuits, mechanical translational/rotational systems, and electro-mechanical systems using differential equations. It covers block diagram representation and reduction of mathematical models. The overall goal is to realize the importance of deriving accurate mathematical models for analyzing and designing control systems.
State space analysis, eign values and eign vectorsShilpa Shukla
This document discusses state space analysis and the conversion of transfer functions to state space models. It covers:
1. The need to convert transfer functions to state space form in order to apply modern time domain techniques for system analysis and design.
2. Three possible representations for realizing a transfer function as a state space model: first companion form, second companion form, and Jordan canonical form.
3. The concepts of eigenvalues and eigenvectors, and how they relate to state space models.
4. Worked examples of converting transfer functions to state space models in first and second companion forms, as well as the Jordan canonical form for systems with repeated and non-repeated roots.
The document provides an overview
This document discusses state space analysis and related concepts. It defines state as a group of variables that summarize a system's history to predict future outputs. The minimum number of state variables required is equal to the number of storage elements in the system. These state variables form a state vector. The document also covers state space representation, diagonalization, solving state equations, the state transition matrix, and concepts of controllability and observability.
This document discusses and compares the classical/transfer function approach and the state space/modern control approach for modeling dynamical systems. The classical approach uses Laplace transforms and transfer functions in the frequency domain, while the state space approach uses matrices to represent systems of differential equations directly in the time domain. The state space approach can model nonlinear, time-varying, and multi-input multi-output systems and considers initial conditions, while the classical approach is limited to linear time-invariant single-input single-output systems. The document provides examples of modeling circuits using the state space representation.
state space representation,State Space Model Controllability and Observabilit...Waqas Afzal
State Variables of a Dynamical System
State Variable Equation
Why State space approach
Block Diagram Representation Of State Space Model
Controllability and Observability
Derive Transfer Function from State Space Equation
Time Response and State Transition Matrix
Eigen Value
This document provides an overview of mathematical modeling of electrical and electronic systems. It discusses:
- The basic elements of electrical systems including resistors, capacitors, and inductors and their voltage-current relationships.
- Examples of modeling simple RC and RLC circuits and calculating their transfer functions.
- Operational amplifiers and examples of inverting and non-inverting configurations.
- Worked examples of calculating transfer functions for various circuits containing resistors, capacitors, inductors, and operational amplifiers.
Modern Control - Lec07 - State Space Modeling of LTI SystemsAmr E. Mohamed
The document provides an overview of state-space representation of linear time-invariant (LTI) systems. It defines key concepts such as state variables, state vector, state equations, and output equations. Examples are given to show how to derive the state-space models from differential equations describing dynamical systems. Specifically, it shows how to 1) select state variables, 2) write first-order differential equations as state equations, and 3) obtain output equations to fully represent LTI systems in state-space form.
Dcs lec03 - z-analysis of discrete time control systemsAmr E. Mohamed
The document discusses discrete time control systems and their mathematical representation using z-transforms. It covers topics such as impulse sampling, the convolution integral method for obtaining the z-transform, properties of the z-transform, inverse z-transforms using long division and partial fractions, and mapping between the s-plane and z-plane. Examples are provided to illustrate various concepts around discrete time systems and their analysis using z-transforms.
Transfer function of Mechanical translational system KALPANA K
This document discusses transfer functions and mechanical translational systems. It contains the following key points:
1) The transfer function is defined as the ratio of the Laplace transform of the output variable to the Laplace transform of the input variable, assuming zero initial conditions.
2) Mechanical translational systems can be modeled using mass, spring, and dashpot elements. Forces acting on these elements are modeled using Newton's second law.
3) Examples are provided to illustrate how to write the differential equations for a mechanical system, take the Laplace transform to obtain algebraic equations, and determine the transfer function as the ratio of the output to input variable.
Chapter3 - Fourier Series Representation of Periodic SignalsAttaporn Ninsuwan
This document discusses Fourier series representation of periodic signals. It introduces continuous-time periodic signals and their representation as a linear combination of harmonically related complex exponentials. The coefficients in the Fourier series representation can be determined by multiplying both sides of the representation by complex exponentials and integrating over one period. The key steps are: 1) multiplying both sides by e-jω0t, 2) integrating both sides from 0 to T=2π/ω0, and 3) using the fact that the integral equals T when k=n and 0 otherwise to obtain an expression for the coefficients an. Examples are provided to illustrate these concepts.
First & second order of the control systemsSATHEESH C S
This document summarizes key concepts about first and second order control systems. It discusses:
- The characteristics of a first order system, which has one pole and is defined by its DC gain (K) and time constant (T).
- Examples of first order systems and how to determine their DC gain and time constant.
- That a second order system can have different responses depending on its parameters, such as damped or undamped oscillations.
- How to determine the undamped natural frequency and damping ratio of a second order system by comparing its transfer function to the general second order transfer function.
The document then provides example problems for determining properties of first and second order systems. It concludes by
This document discusses linear time-invariant (LTI) systems in discrete time. It introduces the convolution sum representation of LTI systems, where the output of an LTI system with impulse response h[n] and input x[n] is given by y[n]=x[n]*h[n]=∑k x[k]h[n-k]. Several examples are worked through to demonstrate calculating the output of an LTI system given its impulse response and input. The document also discusses representing discrete time signals as the sum of shifted unit impulse functions and properties of LTI systems like time-invariance.
This document discusses nonlinear systems and their behavior. Nonlinear systems are represented by nonlinear differential equations and do not obey the principle of superposition. Their response depends on both the input amplitude and initial state. Nonlinear systems can exhibit phenomena like jump resonance, limit cycles, and asynchronous quenching. Nonlinearities can be incidental, inherently present in systems, or intentional, deliberately inserted. Examples of nonlinearities include saturation, dead zones, relays, and multivariable nonlinearities.
This document discusses steady-state error in control systems. It defines steady-state error and describes how it arises from system configuration and input type. Examples are provided to illustrate calculating steady-state error for various system types and inputs, including step, ramp, and disturbances. Sensitivity analysis is also introduced to analyze how changes in system parameters affect steady-state error.
This document discusses steady state error in control systems. It defines steady state error as the difference between the input and output of a system at infinite time. The type of a control system, from Type 0 to higher, determines its steady state error for different input types like steps, ramps, and parabolas. Higher type systems have lower steady state error but reduced stability. The document also introduces static error constants that quantify steady state error for different input types, like position (Kp) for steps, velocity (Kv) for ramps, and acceleration (Ka) for parabolas. These constants are used to calculate the expected steady state error for a given system and input.
This presentation gives complete idea about time domain analysis of first and second order system, type number, time domain specifications, steady state error and error constants and numerical examples.
THIS PPT IS SO USEFUL FOR THE CONTROL SYSTEM STUDENTS MOSTLY. THIS PPT MAINLY DISCUSSED ABOUT THE IMPULSE RESPONSE OF SECOND ORDER SYSTEM
AND THE CHARACTERISTICS OF THE SYSTEM AND STABILITY FACTOR OF THE SYSTEM AN THIS PPT CONTAINS A MATLAB CODING AND SIMULATION AND THE RESULTS ARE ALSO PLOTED IN THE PPT . SO IT IS SO USEFUL TO THE STUDENTS
This document provides an overview of digital filter design. It introduces finite impulse response (FIR) and infinite impulse response (IIR) filters. FIR filters are designed using window techniques like rectangular, Hamming, and Kaiser windows. IIR filters are designed using approximation methods like Butterworth, Chebyshev I, and Chebyshev II. MATLAB code is provided to design low pass, high pass, and other filters using different window and approximation techniques. Pros and cons of FIR and IIR filters are discussed along with references.
This document discusses discrete-time signals and sequences. It defines discrete-time signals as sequences of numbers represented as x[n], where n is an integer. In practice, sequences arise from periodically sampling an analog signal. Linear time-invariant (LTI) systems are described by the convolution sum, where the impulse response h[n] completely characterizes the system. FIR systems have impulse responses of finite duration, while IIR systems can have impulse responses that extend to infinity.
This document discusses signal flow graphs and Mason's rule for calculating transfer functions from such graphs. It defines key terms used in signal flow graphs like nodes, branches, paths, loops, and gains. Mason's rule is presented as a way to relate a signal flow graph to the simultaneous equations of the system to determine the overall transfer function T. The formula for T is given as the sum of the products of the forward path gains and the characteristic function Δ over the number of paths. An example is worked through to demonstrate applying Mason's rule.
The document provides information about time domain analysis of first order systems. It discusses key concepts such as impulse response, step response, and ramp response of first order systems. It also discusses how to determine the transfer function of a first order system based on its step response obtained from practical testing. Examples of first order systems including DC motor and electrical circuits are also provided. The document analyzes various properties of first order systems such as effect of a zero, comparison of responses with and without zero, and response of a system with time delay. Matlab commands for partial fraction expansion are also explained.
The Nyquist stability criterion examines the stability of a linear control system by analyzing the contour of the open-loop transfer function G(s)H(s) in the complex plane. If the contour encircles the point -1+j0 in an anticlockwise direction the same number of times as the number of poles of G(s)H(s) in the right half plane, then the closed-loop system is stable. If there is no encirclement of -1+j0, the system is stable if there are no right half plane poles, and unstable if there are. Clockwise encirclement of -1+j0 always results in an unstable system. The criterion can be used
Modern Control - Lec 03 - Feedback Control Systems Performance and Characteri...Amr E. Mohamed
The document summarizes key concepts about feedback control systems including:
- It defines the order of a system as the highest power of s in the denominator of the transfer function. First and second order systems are discussed.
- Standard test signals like impulse, step, ramp and parabolic are introduced to analyze the response of systems.
- The time response of systems has transient and steady-state components. Poles determine the transient response.
- For first order systems, the responses to unit impulse, step, and ramp inputs are derived. The step response reaches 63.2% of its final value after one time constant.
- For second order systems, the natural frequency, damping ratio, and poles are defined.
Modern Control - Lec 01 - Introduction to Control SystemAmr E. Mohamed
This document provides an introduction to control systems. It begins by stating the objectives of describing the process of designing a control system and examining examples. It then defines what is meant by "control" and provides everyday examples. Automatic control is discussed as playing a vital role in engineering applications like robotics, transportation and industrial processes. The key difference between open-loop and closed-loop control systems is explained, with closed-loop systems being able to account for disturbances but being more complex. Key terms are defined and examples of control systems for liquid level, CD player speed, temperature and antenna position are described.
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.
block diagram representation of control systemsAhmed Elmorsy
This document provides an introduction to block diagram representation of control systems. It discusses how block diagrams provide a pictorial representation of the relationships between elements in a system using blocks and arrows. The blocks represent system elements or operations, and the arrows represent the direction of signal or information flow. Specific topics covered include summing points, takeoff points, examples of representing equations as block diagrams, and canonical forms.
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.
Mechanical translational rotational systems and electrical analogous circuit...SATHEESH C S
Mr. C.S.Satheesh, M.E.,
Mechanical Translational and Rotational Systems and Electrical analogous Circuits in control systems
Spring
Dash-pot
Analogous electrical elements in torque current analogy for the elements of mechanical rotational system.
Electrical systems
Transfer function of Mechanical translational system KALPANA K
This document discusses transfer functions and mechanical translational systems. It contains the following key points:
1) The transfer function is defined as the ratio of the Laplace transform of the output variable to the Laplace transform of the input variable, assuming zero initial conditions.
2) Mechanical translational systems can be modeled using mass, spring, and dashpot elements. Forces acting on these elements are modeled using Newton's second law.
3) Examples are provided to illustrate how to write the differential equations for a mechanical system, take the Laplace transform to obtain algebraic equations, and determine the transfer function as the ratio of the output to input variable.
Chapter3 - Fourier Series Representation of Periodic SignalsAttaporn Ninsuwan
This document discusses Fourier series representation of periodic signals. It introduces continuous-time periodic signals and their representation as a linear combination of harmonically related complex exponentials. The coefficients in the Fourier series representation can be determined by multiplying both sides of the representation by complex exponentials and integrating over one period. The key steps are: 1) multiplying both sides by e-jω0t, 2) integrating both sides from 0 to T=2π/ω0, and 3) using the fact that the integral equals T when k=n and 0 otherwise to obtain an expression for the coefficients an. Examples are provided to illustrate these concepts.
First & second order of the control systemsSATHEESH C S
This document summarizes key concepts about first and second order control systems. It discusses:
- The characteristics of a first order system, which has one pole and is defined by its DC gain (K) and time constant (T).
- Examples of first order systems and how to determine their DC gain and time constant.
- That a second order system can have different responses depending on its parameters, such as damped or undamped oscillations.
- How to determine the undamped natural frequency and damping ratio of a second order system by comparing its transfer function to the general second order transfer function.
The document then provides example problems for determining properties of first and second order systems. It concludes by
This document discusses linear time-invariant (LTI) systems in discrete time. It introduces the convolution sum representation of LTI systems, where the output of an LTI system with impulse response h[n] and input x[n] is given by y[n]=x[n]*h[n]=∑k x[k]h[n-k]. Several examples are worked through to demonstrate calculating the output of an LTI system given its impulse response and input. The document also discusses representing discrete time signals as the sum of shifted unit impulse functions and properties of LTI systems like time-invariance.
This document discusses nonlinear systems and their behavior. Nonlinear systems are represented by nonlinear differential equations and do not obey the principle of superposition. Their response depends on both the input amplitude and initial state. Nonlinear systems can exhibit phenomena like jump resonance, limit cycles, and asynchronous quenching. Nonlinearities can be incidental, inherently present in systems, or intentional, deliberately inserted. Examples of nonlinearities include saturation, dead zones, relays, and multivariable nonlinearities.
This document discusses steady-state error in control systems. It defines steady-state error and describes how it arises from system configuration and input type. Examples are provided to illustrate calculating steady-state error for various system types and inputs, including step, ramp, and disturbances. Sensitivity analysis is also introduced to analyze how changes in system parameters affect steady-state error.
This document discusses steady state error in control systems. It defines steady state error as the difference between the input and output of a system at infinite time. The type of a control system, from Type 0 to higher, determines its steady state error for different input types like steps, ramps, and parabolas. Higher type systems have lower steady state error but reduced stability. The document also introduces static error constants that quantify steady state error for different input types, like position (Kp) for steps, velocity (Kv) for ramps, and acceleration (Ka) for parabolas. These constants are used to calculate the expected steady state error for a given system and input.
This presentation gives complete idea about time domain analysis of first and second order system, type number, time domain specifications, steady state error and error constants and numerical examples.
THIS PPT IS SO USEFUL FOR THE CONTROL SYSTEM STUDENTS MOSTLY. THIS PPT MAINLY DISCUSSED ABOUT THE IMPULSE RESPONSE OF SECOND ORDER SYSTEM
AND THE CHARACTERISTICS OF THE SYSTEM AND STABILITY FACTOR OF THE SYSTEM AN THIS PPT CONTAINS A MATLAB CODING AND SIMULATION AND THE RESULTS ARE ALSO PLOTED IN THE PPT . SO IT IS SO USEFUL TO THE STUDENTS
This document provides an overview of digital filter design. It introduces finite impulse response (FIR) and infinite impulse response (IIR) filters. FIR filters are designed using window techniques like rectangular, Hamming, and Kaiser windows. IIR filters are designed using approximation methods like Butterworth, Chebyshev I, and Chebyshev II. MATLAB code is provided to design low pass, high pass, and other filters using different window and approximation techniques. Pros and cons of FIR and IIR filters are discussed along with references.
This document discusses discrete-time signals and sequences. It defines discrete-time signals as sequences of numbers represented as x[n], where n is an integer. In practice, sequences arise from periodically sampling an analog signal. Linear time-invariant (LTI) systems are described by the convolution sum, where the impulse response h[n] completely characterizes the system. FIR systems have impulse responses of finite duration, while IIR systems can have impulse responses that extend to infinity.
This document discusses signal flow graphs and Mason's rule for calculating transfer functions from such graphs. It defines key terms used in signal flow graphs like nodes, branches, paths, loops, and gains. Mason's rule is presented as a way to relate a signal flow graph to the simultaneous equations of the system to determine the overall transfer function T. The formula for T is given as the sum of the products of the forward path gains and the characteristic function Δ over the number of paths. An example is worked through to demonstrate applying Mason's rule.
The document provides information about time domain analysis of first order systems. It discusses key concepts such as impulse response, step response, and ramp response of first order systems. It also discusses how to determine the transfer function of a first order system based on its step response obtained from practical testing. Examples of first order systems including DC motor and electrical circuits are also provided. The document analyzes various properties of first order systems such as effect of a zero, comparison of responses with and without zero, and response of a system with time delay. Matlab commands for partial fraction expansion are also explained.
The Nyquist stability criterion examines the stability of a linear control system by analyzing the contour of the open-loop transfer function G(s)H(s) in the complex plane. If the contour encircles the point -1+j0 in an anticlockwise direction the same number of times as the number of poles of G(s)H(s) in the right half plane, then the closed-loop system is stable. If there is no encirclement of -1+j0, the system is stable if there are no right half plane poles, and unstable if there are. Clockwise encirclement of -1+j0 always results in an unstable system. The criterion can be used
Modern Control - Lec 03 - Feedback Control Systems Performance and Characteri...Amr E. Mohamed
The document summarizes key concepts about feedback control systems including:
- It defines the order of a system as the highest power of s in the denominator of the transfer function. First and second order systems are discussed.
- Standard test signals like impulse, step, ramp and parabolic are introduced to analyze the response of systems.
- The time response of systems has transient and steady-state components. Poles determine the transient response.
- For first order systems, the responses to unit impulse, step, and ramp inputs are derived. The step response reaches 63.2% of its final value after one time constant.
- For second order systems, the natural frequency, damping ratio, and poles are defined.
Modern Control - Lec 01 - Introduction to Control SystemAmr E. Mohamed
This document provides an introduction to control systems. It begins by stating the objectives of describing the process of designing a control system and examining examples. It then defines what is meant by "control" and provides everyday examples. Automatic control is discussed as playing a vital role in engineering applications like robotics, transportation and industrial processes. The key difference between open-loop and closed-loop control systems is explained, with closed-loop systems being able to account for disturbances but being more complex. Key terms are defined and examples of control systems for liquid level, CD player speed, temperature and antenna position are described.
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.
block diagram representation of control systemsAhmed Elmorsy
This document provides an introduction to block diagram representation of control systems. It discusses how block diagrams provide a pictorial representation of the relationships between elements in a system using blocks and arrows. The blocks represent system elements or operations, and the arrows represent the direction of signal or information flow. Specific topics covered include summing points, takeoff points, examples of representing equations as block diagrams, and canonical forms.
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.
Mechanical translational rotational systems and electrical analogous circuit...SATHEESH C S
Mr. C.S.Satheesh, M.E.,
Mechanical Translational and Rotational Systems and Electrical analogous Circuits in control systems
Spring
Dash-pot
Analogous electrical elements in torque current analogy for the elements of mechanical rotational system.
Electrical systems
Mechanical translational rotational systems and electrical analogous circuit...SATHEESH C S
Similar to modeling of system electrical, Basic Elements Modeling-R,L,C Solved Examples with RLC circuit L, C Modeling with Non-Zero Initial condition (20)
modeling of system electromechanical, Armature Controlled D.C Motor -Reduced ...Waqas Afzal
The document summarizes mathematical models of DC motor systems. It describes:
1) An armature-controlled DC motor model including electrical and mechanical subsystems and derived transfer functions relating input voltage to output speed and position.
2) A reduced-order model of the armature-controlled motor assuming small inductance.
3) A field-controlled DC motor model describing the electrical and mechanical subsystems and derived transfer functions relating input field voltage to output speed and position.
Mathematical modeling electric circuits and Transfer FunctionTeerawutSavangboon
The document describes the mathematical modeling of electrical components like resistors, inductors and capacitors using Laplace transforms. It provides an example of modeling an RC circuit. The RC circuit is represented by two equations in the time domain, which are then transformed to the s-domain using Laplace transforms. This yields the transfer function of the circuit, relating the output voltage to the input current. The circuit and transfer function are then represented as a block diagram.
This document provides an overview of mathematical modeling of electrical and electronic systems. It discusses the basic elements of electrical systems including resistors, capacitors, and inductors. It provides the voltage-current relationships and Laplace transforms for each element. Examples are presented on finding transfer functions for circuits containing resistors, capacitors, and inductors. The document also discusses operational amplifiers and provides examples of determining transfer functions for circuits using op-amps, including inverting and non-inverting configurations.
The document discusses transient responses in first-order circuits. It begins by defining transients and steady state, and notes that first-order circuits can be characterized by first-order differential equations. It then lists several rules regarding how voltage and current cannot change instantly in capacitors and inductors. The document proceeds to provide examples of solving for the step response of RC and RL circuits using node/loop equations. It also discusses the response to rectangular pulse inputs by breaking them into step functions. Finally, it summarizes key concepts around transients in first-order circuits.
The document discusses time domain analysis and standard test signals used to analyze dynamic systems. It describes the impulse, step, ramp, and parabolic signals which imitate characteristics of actual inputs such as sudden shock, sudden change, constant velocity, and constant acceleration. The time response of first order systems to these standard inputs is expressed mathematically. The impulse response directly provides the system transfer function. Step response reaches 63% of its final value within one time constant.
Design and implementation of cyclo converter for high frequency applicationscuashok07
This document presents a design and implementation of a 3-phase cyclo-converter for high frequency applications. It uses an H-bridge inverter to generate a constant voltage at an RLC load. MOSFETs are used as switching devices due to their high switching speed. The purpose is to convert low frequency AC to high frequency AC without switching losses. MATLAB Simulink and Keil software are used to simulate the power and control circuits respectively.
The document provides an introduction to electricity, including its behavior at the atomic level. It describes how electricity is created through the movement of electrons between atoms. It also explains key concepts in electrical circuits such as voltage, current, resistance, and Ohm's law. Circuit configurations such as series and parallel are defined, and equations like Kirchhoff's laws are presented for analyzing circuits.
This document provides an introduction to electricity, including:
1) It explains electricity at the atomic level, describing atoms, protons, neutrons, electrons, and electron orbitals.
2) It introduces concepts of conductors and insulators, explaining that conductors have 1-3 valence electrons allowing electron flow between atoms, while insulators have 5-8 valence electrons making flow difficult.
3) It describes the basics of electrical circuits, including current, voltage, resistance, and Ohm's Law, and how to measure these properties with a multimeter. Kirchhoff's Laws for series and parallel circuits are also introduced.
The document provides an introduction to electricity, including its behavior at the atomic level. It describes how electricity is created through the movement of electrons between atoms. It also explains key concepts in electrical circuits such as voltage, current, resistance, and Ohm's law. Circuit configurations such as series and parallel are defined, and equations like Kirchhoff's laws are presented for analyzing circuits.
Lecture slides Ist & 2nd Order Circuits[282].pdfsami717280
- The document discusses first-order differential circuits that contain a single storage element like a capacitor or inductor. It describes how to analyze such circuits by examining their behavior over time after a switch opens or closes.
- The time constant, represented by tau (τ), is defined as the time required for the storing element in a circuit to charge. Common time constants include L/R for inductors and RC for capacitors.
- Differential equations can be used to model first-order circuits and solutions involve finding the particular integral and complementary solutions based on initial conditions.
The document discusses the application of Laplace transforms to solve electric circuit problems. It begins with an introduction to Laplace transforms and their properties. It then shows how to convert a simple RLC electric circuit into a second order differential equation. The document works through an example problem of finding the current and charge in an RLC circuit over time using Laplace transforms. It demonstrates taking the Laplace transform of the differential equation, solving for the transformed current and charge, and applying the inverse Laplace transform to find the current and charge as a function of time.
The document discusses the Z-transform, which is the discrete-time equivalent of the Laplace transform. Some key points covered include:
- The Z-transform is used to analyze discrete-time signals and discrete-time systems. It allows representation in the frequency domain through pole-zero analysis.
- Causal, anti-causal, and two-sided sequences have different regions of convergence for their Z-transforms. Stability depends on poles lying inside or outside the unit circle.
- The Z-transform has various properties that allow computations and transformations. The inverse Z-transform can be obtained through techniques like partial fraction expansion or long division.
Why Fourier Transform
General Properties & Symmetry relations
Formula and steps
magnitude and phase spectra
Convergence Condition
mean-square convergence
Gibbs phenomenon
Direct Delta
Energy Density Spectrum
Frequency modulation (FM) is a type of angle modulation where the instantaneous frequency of the carrier signal varies linearly with the modulating signal. There are two types of FM: narrowband FM (NBFM) where the modulation index is less than 1, and wideband FM (WBFM) where the modulation index is greater than 1. The bandwidth of an FM signal can be estimated using Carson's rule, which states that nearly all the signal power lies within a bandwidth equal to twice the maximum frequency deviation plus the maximum modulating frequency. FM signals have constant amplitude but varying frequency, so their average power does not depend on the modulating signal and remains constant.
ROOT-LOCUS METHOD, Determine the root loci on the real axis /the asymptotes o...Waqas Afzal
Angle and Magnitude Conditions
Example of Root Locus
Steps
constructing a root-locus plot is to locate the open-loop poles and zeros in s-plane.
Determine the root loci on the real axis
Determine the asymptotes of the root loci
Determine the breakaway point.
Closed loop stability via root locus
This document provides an overview of programmable logic controllers (PLCs), including their basic components, programming languages, and common instructions. PLCs are digital computers used to control industrial automation processes. They have input and output modules to interface with external devices, and can be programmed using ladder logic or other languages to implement control functions. Common instructions include timers, counters, and bit logic operations to automate industrial processes.
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
modeling of system electronics, Operational Amplifier Basics Solved Examples ...Waqas Afzal
This document discusses modeling electronic systems using operational amplifiers. It provides operational amplifier basics, examples of calculating transfer functions of operational amplifier circuits, and discusses using operational amplifiers for lead/lag compensation and PID controllers. Specifically, it gives examples of calculating transfer functions, discusses electric network transfer functions, and provides a table of common operational amplifier circuits that can be used as controllers or compensators.
modeling of system rotational, Basic Elements Modeling-Spring(K), Damper(D), ...Waqas Afzal
The document discusses modeling of rotational mechanical systems. It covers basic elements like springs, dampers, and inertia. It provides the equations of motion for rotational systems involving these elements. Examples are given of modeling systems with multiple rotational elements connected by springs and dampers. The document also discusses modeling of gear systems, including the fundamental properties of gears, calculating gear ratio based on the number of teeth, and the mathematical relationship between the angular velocities of connected gears.
modeling of MECHANICAL system (translational), Basic Elements Modeling-Spring...Waqas Afzal
This document summarizes modeling of mechanical translational systems. It discusses modeling basic elements like springs, masses, and dampers and provides their equations of motion. Examples are given of modeling multiple springs, masses and dampers connected together in different configurations. The state equations and state diagram are obtained for a sample mechanical translational system with multiple springs and dampers connecting different masses.
introduction to modeling, Types of Models, Classification of mathematical mod...Waqas Afzal
Types of Systems
Ways to study system
Model
Types of Models
Why Mathematical Model
Classification of mathematical models
Black box, white box, Gray box
Lumped systems
Dynamic Systems
Simulation
laplace transform and inverse laplace, properties, Inverse Laplace Calculatio...Waqas Afzal
Laplace Transform
-Proof of common function
-properties
-Initial Value and Final Value Problems
Inverse Laplace Calculations
-by identification
-Partial fraction
Solution of Ordinary differential using Laplace and inverse Laplace
Transfer Function, Concepts of stability(critical, Absolute & Relative) Poles...Waqas Afzal
Transfer Function
The Order of Control Systems
Concepts of stability(critical, Absolute & Relative)
Poles, Zeros
Stability calculation
BIBO stability
Transient Response Characteristics
Signal Flow Graph, SFG and Mason Gain Formula, Example solved with Masson Gai...Waqas Afzal
Basic Properties of SFG
Definitions of SFG Terms
SFG Algebra
Relation between SFG and block diagram
Mason Gain Formula
Example solved with Masson Gain Formula
The document describes 8 rules for reducing block diagrams:
1) Gains of blocks in cascade are multiplied. Gains of blocks in parallel are added.
2) Feedback loops can be eliminated by expressing the output in terms of the input and the loop gain.
3) Summing points can be rearranged or split using associative laws. Summing points can also be shifted before or after blocks.
Examples show applying the rules to reduce complex block diagrams into simplified expressions relating the output to the input.
automatic control, Basic Definitions, Classification of Control systems, Requ...Waqas Afzal
Why automatic controls is required
2. Process Variables
controlled variable, manipulated variable
3. Functions of Automatic Control
Measurement
Comparison
Computation
Correction
4.Basic Definitions
System, Plant, Process, Controller, input, output, disturbance
5. Classification of Control systems
Natural, Manmade & Automatic control system
Open-Loop, Close-Loop control System
Linear Vs Nonlinear System
Time invariant vs Time variant
Continuous Data Vs Discrete Data System
Deterministic vs Stochastic System
6. Requirements of an ideal Control system
Accuracy, Sensitivity, noise, Bandwidth, Speed, Oscillations
Welcome to the May 2025 edition of WIPAC Monthly celebrating the 14th anniversary of the WIPAC Group and WIPAC monthly.
In this edition along with the usual news from around the industry we have three great articles for your contemplation
Firstly from Michael Dooley we have a feature article about ammonia ion selective electrodes and their online applications
Secondly we have an article from myself which highlights the increasing amount of wastewater monitoring and asks "what is the overall" strategy or are we installing monitoring for the sake of monitoring
Lastly we have an article on data as a service for resilient utility operations and how it can be used effectively.
この資料は、Roy FieldingのREST論文(第5章)を振り返り、現代Webで誤解されがちなRESTの本質を解説しています。特に、ハイパーメディア制御やアプリケーション状態の管理に関する重要なポイントをわかりやすく紹介しています。
This presentation revisits Chapter 5 of Roy Fielding's PhD dissertation on REST, clarifying concepts that are often misunderstood in modern web design—such as hypermedia controls within representations and the role of hypermedia in managing application state.
Introduction to ANN, McCulloch Pitts Neuron, Perceptron and its Learning
Algorithm, Sigmoid Neuron, Activation Functions: Tanh, ReLu Multi- layer Perceptron
Model – Introduction, learning parameters: Weight and Bias, Loss function: Mean
Square Error, Back Propagation Learning Convolutional Neural Network, Building
blocks of CNN, Transfer Learning, R-CNN,Auto encoders, LSTM Networks, Recent
Trends in Deep Learning.
This research is oriented towards exploring mode-wise corridor level travel-time estimation using Machine learning techniques such as Artificial Neural Network (ANN) and Support Vector Machine (SVM). Authors have considered buses (equipped with in-vehicle GPS) as the probe vehicles and attempted to calculate the travel-time of other modes such as cars along a stretch of arterial roads. The proposed study considers various influential factors that affect travel time such as road geometry, traffic parameters, location information from the GPS receiver and other spatiotemporal parameters that affect the travel-time. The study used a segment modeling method for segregating the data based on identified bus stop locations. A k-fold cross-validation technique was used for determining the optimum model parameters to be used in the ANN and SVM models. The developed models were tested on a study corridor of 59.48 km stretch in Mumbai, India. The data for this study were collected for a period of five days (Monday-Friday) during the morning peak period (from 8.00 am to 11.00 am). Evaluation scores such as MAPE (mean absolute percentage error), MAD (mean absolute deviation) and RMSE (root mean square error) were used for testing the performance of the models. The MAPE values for ANN and SVM models are 11.65 and 10.78 respectively. The developed model is further statistically validated using the Kolmogorov-Smirnov test. The results obtained from these tests proved that the proposed model is statistically valid.
Newly poured concrete opposing hot and windy conditions is considerably susceptible to plastic shrinkage cracking. Crack-free concrete structures are essential in ensuring high level of durability and functionality as cracks allow harmful instances or water to penetrate in the concrete resulting in structural damages, e.g. reinforcement corrosion or pressure application on the crack sides due to water freezing effect. Among other factors influencing plastic shrinkage, an important one is the concrete surface humidity evaporation rate. The evaporation rate is currently calculated in practice by using a quite complex Nomograph, a process rather tedious, time consuming and prone to inaccuracies. In response to such limitations, three analytical models for estimating the evaporation rate are developed and evaluated in this paper on the basis of the ACI 305R-10 Nomograph for “Hot Weather Concreting”. In this direction, several methods and techniques are employed including curve fitting via Genetic Algorithm optimization and Artificial Neural Networks techniques. The models are developed and tested upon datasets from two different countries and compared to the results of a previous similar study. The outcomes of this study indicate that such models can effectively re-develop the Nomograph output and estimate the concrete evaporation rate with high accuracy compared to typical curve-fitting statistical models or models from the literature. Among the proposed methods, the optimization via Genetic Algorithms, individually applied at each estimation process step, provides the best fitting result.
This research presents the optimization techniques for reinforced concrete waffle slab design because the EC2 code cannot provide an efficient and optimum design. Waffle slab is mostly used where there is necessity to avoid column interfering the spaces or for a slab with large span or as an aesthetic purpose. Design optimization has been carried out here with MATLAB, using genetic algorithm. The objective function include the overall cost of reinforcement, concrete and formwork while the variables comprise of the depth of the rib including the topping thickness, rib width, and ribs spacing. The optimization constraints are the minimum and maximum areas of steel, flexural moment capacity, shear capacity and the geometry. The optimized cost and slab dimensions are obtained through genetic algorithm in MATLAB. The optimum steel ratio is 2.2% with minimum slab dimensions. The outcomes indicate that the design of reinforced concrete waffle slabs can be effectively carried out using the optimization process of genetic algorithm.
Construction Materials (Paints) in Civil EngineeringLavish Kashyap
This file will provide you information about various types of Paints in Civil Engineering field under Construction Materials.
It will be very useful for all Civil Engineering students who wants to search about various Construction Materials used in Civil Engineering field.
Paint is a vital construction material used for protecting surfaces and enhancing the aesthetic appeal of buildings and structures. It consists of several components, including pigments (for color), binders (to hold the pigment together), solvents or thinners (to adjust viscosity), and additives (to improve properties like durability and drying time).
Paint is one of the material used in Civil Engineering field. It is especially used in final stages of construction project.
Paint plays a dual role in construction: it protects building materials and contributes to the overall appearance and ambiance of a space.
modeling of system electrical, Basic Elements Modeling-R,L,C Solved Examples with RLC circuit L, C Modeling with Non-Zero Initial condition
1. Modeling of Electrical System
•Basic Elements Modeling-R,L,C
•Solved Examples with RLC circuit
•L, C Modeling with Non-Zero Initial condition
1
2. Modeling of Electrical System
• The time domain expression relating voltage and current for the
resistor is given by Ohm’s law i-e
R
t
i
t
v R
R )
(
)
(
• The Laplace transform of the above equation is
R
s
I
s
V R
R )
(
)
(
R
s
I
s
V R
R
)
(
/
)
(
3. Basic Elements of Electrical Systems
• The time domain expression relating voltage and current for the
Capacitor is given as:
dt
t
i
C
t
v c
c
)
(
)
(
1
• The Laplace transform of the above equation (assuming there is no
charge stored in the capacitor) is
)
(
)
( s
I
Cs
s
V c
c
1
Cs
s
I
s
V c
c
1
)
(
/
)
(
4. Basic Elements of Electrical Systems
• The time domain expression relating voltage and current for the
inductor is given as:
dt
t
di
L
t
v L
L
)
(
)
(
• The Laplace transform of the above equation (assuming there is no
energy stored in inductor) is
)
(
)
( s
LsI
s
V L
L
Ls
s
I
s
V L
L
)
(
/
)
(
5. V-I and I-V relations
5
Component Laplace V-I Relation I-V Relation
Resistor R
Capacitor
Inductor
dt
t
di
L
t
v L
L
)
(
)
(
dt
t
i
C
t
v c
c
)
(
)
(
1
R
t
i
t
v R
R )
(
)
(
R
t
v
t
i R
R
)
(
)
(
dt
t
dv
C
t
i c
c
)
(
)
(
dt
t
v
L
t
i L
L
)
(
)
(
1
Ls
Cs
1
6. Example-1
• The two-port network shown in the following figure has vi(t) as
the input voltage and vo(t) as the output voltage. Find the
transfer function Vo(s)/Vi(s) of the network.
6
C
i(t)
vi( t) vo(t)
dt
t
i
C
R
t
i
t
vi )
(
)
(
)
(
1
dt
t
i
C
t
vo )
(
)
(
1
7. Example-1
• Taking Laplace transform of both equations, considering initial
conditions to zero.
• Re-arrange both equations as:
7
dt
t
i
C
R
t
i
t
vi )
(
)
(
)
(
1
dt
t
i
C
t
vo )
(
)
(
1
)
(
)
(
)
( s
I
Cs
R
s
I
s
Vi
1
)
(
)
( s
I
Cs
s
Vo
1
Cs
s
I
s
V
s
I
s
CsV
o
o
/
)
(
)
(
)
(
)
(
)
)(
(
)
(
Cs
R
s
I
s
Vi
1
8. Example-1
• .
8
Cs
s
I
s
Vo /
)
(
)
(
)
)(
(
)
(
Cs
R
s
I
s
Vi
1
)
(
/
)
( s
V
s
V
nction
TransferFu i
o
)
1
)(
(
/
)
(
)
(
)
(
Cs
R
s
I
Cs
s
I
s
V
s
V
i
o
RCs
s
V
s
V
i
o
1
1
)
(
)
(
)
1
(
1
)
(
)
(
Cs
R
Cs
s
V
s
V
i
o
16. Circuit theory problem:
+
_
vc(t) i(t)
3 k
100 F
6 k
0
5
)
(
0
6
^
10
*
100
*
3
^
10
*
2
)
(
0
)
(
0
)
(
)
(
)
(
)
(
)
(
)
(
)
arg
(
)
(
)
(
t
v
dt
t
dv
t
v
dt
t
dv
RC
t
v
dt
t
dv
t
v
dt
t
dv
RC
dt
t
dv
RC
t
v
R
t
i
t
v
ing
disch
dt
t
dv
C
t
i
c
c
c
c
c
c
c
c
c
c
c
c
c
c
Take the Laplace transform
of this equations including
the initial conditions on vc(t)
18. An inductor in the s domain
iv-relation in the time domain
v(t) L
d
i(t).
dt
By operational Laplace transform:
Lv(t) LLi(t) L Li(t),
V(s) LsI(s) I0 sL I(s) LI0.
initial current
1
19. Equivalent circuit of an inductor
Series equivalent: Parallel equivalent:
Thévenin
Norton
1
20. A capacitor in the s domain
iv-relation in the time domain
i(t) C
d
v(t).
dt
By operational Laplace transform:
Li(t) LCv(t) C Lv(t),
I(s) C sV(s) V0 sC V (s) CV0.
initial voltage
2
21. Equivalent circuit of a capacitor
Parallel equivalent: Series equivalent:
Norton
Thévenin
2