LECTURE 33: DIFFERENCE EQUATIONS

Slides:



Advertisements
Similar presentations
Solving Differential Equations BIOE Solving Differential Equations Ex. Shock absorber with rigid massless tire Start with no input r(t)=0, assume.
Advertisements

LAPLACE TRANSFORMS.
Signal Processing in the Discrete Time Domain Microprocessor Applications (MEE4033) Sogang University Department of Mechanical Engineering.
ECE 8443 – Pattern Recognition EE 3512 – Signals: Continuous and Discrete Objectives: Response to a Sinusoidal Input Frequency Analysis of an RC Circuit.
Lecture 7: Basis Functions & Fourier Series
ECE 8443 – Pattern Recognition EE 3512 – Signals: Continuous and Discrete Objectives: Filters and Difference Equations Signal Flow Graphs FIR and IIR Filters.
Familiar Properties of Linear Transforms
Chapter 7 Laplace Transforms. Applications of Laplace Transform notes Easier than solving differential equations –Used to describe system behavior –We.
Department of EECS University of California, Berkeley EECS 105 Fall 2003, Lecture 2 Lecture 2: Transfer Functions Prof. Niknejad.
MM3FC Mathematical Modeling 3 LECTURE 7 Times Weeks 7,8 & 9. Lectures : Mon,Tues,Wed 10-11am, Rm.1439 Tutorials : Thurs, 10am, Rm. ULT. Clinics : Fri,
EE-2027 SaS, L15 1/15 Lecture 15: Continuous-Time Transfer Functions 6 Transfer Function of Continuous-Time Systems (3 lectures): Transfer function, frequency.
Lecture 14: Laplace Transform Properties
The z Transform M. J. Roberts - All Rights Reserved. Edited by Dr. Robert Akl.
Z-Transform Fourier Transform z-transform. Z-transform operator: The z-transform operator is seen to transform the sequence x[n] into the function X{z},
EE513 Audio Signals and Systems Digital Signal Processing (Systems) Kevin D. Donohue Electrical and Computer Engineering University of Kentucky.
ECE 8443 – Pattern Recognition EE 3512 – Signals: Continuous and Discrete Objectives: First-Order Second-Order N th -Order Computation of the Output Signal.
ECE 8443 – Pattern Recognition EE 3512 – Signals: Continuous and Discrete Objectives: Stability and the s-Plane Stability of an RC Circuit 1 st and 2 nd.
Properties of the z-Transform
ECE 8443 – Pattern Recognition ECE 3163 – Signals and Systems Objectives: First-Order Second-Order N th -Order Computation of the Output Signal Transfer.
ECE 8443 – Pattern Recognition ECE 3163 – Signals and Systems Objectives: Modulation Summation Convolution Initial Value and Final Value Theorems Inverse.
Fourier Series. Introduction Decompose a periodic input signal into primitive periodic components. A periodic sequence T2T3T t f(t)f(t)
Rational Transforms Consider the problem of finding the inverse Laplace transform for: where {ai} and {bi} are real numbers, and M and N are positive.
1 Z-Transform. CHAPTER 5 School of Electrical System Engineering, UniMAP School of Electrical System Engineering, UniMAP NORSHAFINASH BT SAUDIN
Signals and Systems 1 Lecture 8 Dr. Ali. A. Jalali September 6, 2002.
Fourier Analysis of Discrete-Time Systems
Motivation Thus far we have dealt primarily with the input/output characteristics of linear systems. State variable, or state space, representations describe.
THE LAPLACE TRANSFORM LEARNING GOALS Definition
ES97H Biomedical Signal Processing
ECE 8443 – Pattern Recognition ECE 3163 – Signals and Systems Objectives: Stability Response to a Sinusoid Filtering White Noise Autocorrelation Power.
Motivation for the Laplace Transform
Lecture 5 – 6 Z - Transform By Dileep Kumar.
ECE 8443 – Pattern Recognition ECE 3163 – Signals and Systems Objectives: Frequency Response Response of a Sinusoid DT MA Filter Filter Design DT WMA Filter.
Fast Fourier Transforms. 2 Discrete Fourier Transform The DFT pair was given as Baseline for computational complexity: –Each DFT coefficient requires.
The Discrete Fourier Transform
Lecture 4: The z-Transform 1. The z-transform The z-transform is used in sampled data systems just as the Laplace transform is used in continuous-time.
ECE 8443 – Pattern Recognition EE 3512 – Signals: Continuous and Discrete Objectives: Difference Equations Transfer Functions Block Diagrams Resources:
ENEE 322: Continuous-Time Fourier Transform (Chapter 4)
Time Domain Representations of Linear Time-Invariant Systems
Chapter 4 Structures for Discrete-Time System Introduction The block diagram representation of the difference equation Basic structures for IIR system.
Lecture 7: Basis Functions & Fourier Series
Properties of the z-Transform
Linear Constant-Coefficient Difference Equations
Lesson 12: Transfer Functions In The Laplace Domain
LECTURE 30: SYSTEM ANALYSIS USING THE TRANSFER FUNCTION
© Dr. Elmer P. Dadios - DLSU Fellow & Professor
CHAPTER 5 Z-Transform. EKT 230.
Discrete-time Systems
LECTURE 11: FOURIER TRANSFORM PROPERTIES
Mathematical Models of Systems Objectives
Linear Prediction Simple first- and second-order systems
Description and Analysis of Systems
UNIT II Analysis of Continuous Time signal
LECTURE 28: THE Z-TRANSFORM AND ITS ROC PROPERTIES
Prof. Vishal P. Jethava EC Dept. SVBIT,Gandhinagar
Research Methods in Acoustics Lecture 9: Laplace Transform and z-Transform Jonas Braasch.
Mechatronics Engineering
Z-Transform ENGI 4559 Signal Processing for Software Engineers
LECTURE 18: FAST FOURIER TRANSFORM
Fundamentals of Electric Circuits Chapter 15
LECTURE 18: FOURIER ANALYSIS OF CT SYSTEMS
LECTURE 39: APPLICATIONS OF STATE VARIABLES
Zhongguo Liu Biomedical Engineering
LECTURE 05: CONVOLUTION OF DISCRETE-TIME SIGNALS
LECTURE 07: CONVOLUTION FOR CT SYSTEMS
LECTURE 20: FOURIER ANALYSIS OF DT SYSTEMS
Laplace Transforms Important analytical method for solving linear ordinary differential equations. - Application to nonlinear ODEs? Must linearize first.
LECTURE 11: FOURIER TRANSFORM PROPERTIES
Laplace Transforms Important analytical method for solving linear ordinary differential equations. - Application to nonlinear ODEs? Must linearize first.
LECTURE 18: FAST FOURIER TRANSFORM
Presentation transcript:

LECTURE 33: DIFFERENCE EQUATIONS Objectives: Difference Equations Transfer Functions Block Diagrams Resources: MIT 6.003: Lecture 23 Wiki: Inverse Z-Transform CNX: Inverse Z-Transform Arslan: The Inverse Z-Transform CNX: Properties ISIP: Pole/Zero Demo MS Equation 3.0 was used with settings of: 18, 12, 8, 18, 12. URL:

First-Order Difference Equations Consider a first-order difference equation: We can apply the time-shift property: We can solve for Y(z): The response is again a function of two things: the response due to the initial condition and the response due to the input. If the initial condition is zero: Applying the inverse z-Transform: Is this system causal? Why? Is this system stable? Why? Suppose the input was a sinusoid. How would you compute the output? ]i=k,

Example of a First-Order System Consider the unit-step response of this system: Use the (1/z) approach for the inverse transform: The output consists of a DC term, an exponential term due to the I.C., and an exponential term due to the input. Under what conditions is the output stable? ]i=k,

Second-Order Difference Equations Consider a second-order difference equation: We can apply the time-shift property: Assume x[-1] = 0 and solve for Y(z): Multiplying z2/z2: Assuming the initial conditions are zero: Note that the impulse response is of the form: This can be visualized as a complex pole pair with a center frequency and bandwidth (see Java applet). ]i=k,

Example of a Second-Order System Consider the unit-step response of this system: We can further simplify this: The inverse z-transform gives: MATLAB: num = [1 -1 0]; den = [1 1.5 .5]; n = 0:20; x = ones(1, length(n)); zi = [-1.5*2-0.5*1, -0.5*2]; y = filter(num, den, x, zi); ]i=k,

Nth-Order Difference Equations Consider a general difference equation: We can apply the time-shift property once again: We can again see the important of poles in the stability and overall frequency response of the system. (See Java applet). Since the coefficients of the denominator are most often real, the transfer function can be factored into a product of complex conjugate poles, which in turn means the impulse response can be computed as the sum of damped sinusoids. Why? The frequency response of the system can be found by setting z = ej. ]i=k,

Transfer Functions In addition to our normal transfer function components, such as summation and multiplication, we use one important additional component: delay. This is often denoted by its z-transform equivalent. We can illustrate this with an example (assume initial conditions are zero): D z-1 ]i=k,

Transfer Function Example Redraw using z-transform: Write equations for the behavior at each of the summation nodes: Three equations and three unknowns: solve the first for Q1(z) and substitute into the other two equations. ]i=k,

Basic Interconnections of Transfer Functions ]i=k,

Summary Demonstrated the solution of Nth-order difference equations using the z-transform: general response is an exponential. Demonstrated how to develop and decompose signal flow graphs using the z-transform: introduced a component, the delay, which is equivalent to differentiation in the s-plane.