Lecture 18: Stability

Similar documents
EEL3135: Homework #4

Lecture 16: Zeros and Poles

Discrete-Time Signals and Systems. The z-transform and Its Application. The Direct z-transform. Region of Convergence. Reference: Sections

Signals and Systems. Problem Set: The z-transform and DT Fourier Transform

Discrete-Time Fourier Transform (DTFT)

University of Illinois at Urbana-Champaign ECE 310: Digital Signal Processing

Stability Condition in Terms of the Pole Locations

Z Transform (Part - II)

Review of Discrete-Time System

Analog vs. discrete signals

ELEG 305: Digital Signal Processing

EE102B Signal Processing and Linear Systems II. Solutions to Problem Set Nine Spring Quarter

Discrete-time signals and systems

Lecture 14: Windowing

Lecture 19 IIR Filters

Digital Signal Processing Lecture 4

LECTURE NOTES DIGITAL SIGNAL PROCESSING III B.TECH II SEMESTER (JNTUK R 13)

Use: Analysis of systems, simple convolution, shorthand for e jw, stability. Motivation easier to write. Or X(z) = Z {x(n)}

Lecture 2 Discrete-Time LTI Systems: Introduction

Lecture 7 - IIR Filters

Chap 2. Discrete-Time Signals and Systems

ECGR4124 Digital Signal Processing Exam 2 Spring 2017

Let H(z) = P(z)/Q(z) be the system function of a rational form. Let us represent both P(z) and Q(z) as polynomials of z (not z -1 )

Solutions. Number of Problems: 10

Review of Fundamentals of Digital Signal Processing

EE301 Signals and Systems In-Class Exam Exam 3 Thursday, Apr. 19, Cover Sheet

Multidimensional digital signal processing

# FIR. [ ] = b k. # [ ]x[ n " k] [ ] = h k. x[ n] = Ae j" e j# ˆ n Complex exponential input. [ ]Ae j" e j ˆ. ˆ )Ae j# e j ˆ. y n. y n.

Modeling and Analysis of Systems Lecture #8 - Transfer Function. Guillaume Drion Academic year

( ) John A. Quinn Lecture. ESE 531: Digital Signal Processing. Lecture Outline. Frequency Response of LTI System. Example: Zero on Real Axis

Transform Analysis of Linear Time-Invariant Systems

ELEG 305: Digital Signal Processing

Digital Signal Processing Lecture 10 - Discrete Fourier Transform

INF3440/INF4440. Design of digital filters

The Z transform (2) 1

Discrete-Time Signals and Systems. Frequency Domain Analysis of LTI Systems. The Frequency Response Function. The Frequency Response Function

Review of Fundamentals of Digital Signal Processing

III. Time Domain Analysis of systems

z-transform Chapter 6

Lecture 04: Discrete Frequency Domain Analysis (z-transform)

ECGR4124 Digital Signal Processing Final Spring 2009

EE 123: Digital Signal Processing Spring Lecture 23 April 10

Discrete Time Signals and Systems Time-frequency Analysis. Gloria Menegaz

ESE 531: Digital Signal Processing

UNIT-II Z-TRANSFORM. This expression is also called a one sided z-transform. This non causal sequence produces positive powers of z in X (z).

Your solutions for time-domain waveforms should all be expressed as real-valued functions.

Lecture 13: Discrete Time Fourier Transform (DTFT)

Like bilateral Laplace transforms, ROC must be used to determine a unique inverse z-transform.

Digital Signal Processing, Homework 1, Spring 2013, Prof. C.D. Chung

Lecture 7 Discrete Systems

Digital Signal Processing Lecture 3 - Discrete-Time Systems

Discrete Time Systems

Solutions: Homework Set # 5

Digital Signal Processing Lecture 5

ELEG 305: Digital Signal Processing

Z-Transform. The Z-transform is the Discrete-Time counterpart of the Laplace Transform. Laplace : G(s) = g(t)e st dt. Z : G(z) =

Digital Signal Processing Lecture 9 - Design of Digital Filters - FIR

Digital Filters Ying Sun

Fourier Series Representation of

ESE 531: Digital Signal Processing

Other types of errors due to using a finite no. of bits: Round- off error due to rounding of products

! z-transform. " Tie up loose ends. " Regions of convergence properties. ! Inverse z-transform. " Inspection. " Partial fraction

Discrete Time Fourier Transform (DTFT)

Discrete-time Signals and Systems in

2. Typical Discrete-Time Systems All-Pass Systems (5.5) 2.2. Minimum-Phase Systems (5.6) 2.3. Generalized Linear-Phase Systems (5.

EE482: Digital Signal Processing Applications

Very useful for designing and analyzing signal processing systems

ECE-314 Fall 2012 Review Questions for Midterm Examination II

Lecture 8 Finite Impulse Response Filters

QUESTION BANK SIGNALS AND SYSTEMS (4 th SEM ECE)

VU Signal and Image Processing

ECE503: Digital Signal Processing Lecture 5

ECE4270 Fundamentals of DSP Lecture 20. Fixed-Point Arithmetic in FIR and IIR Filters (part I) Overview of Lecture. Overflow. FIR Digital Filter

EE123 Digital Signal Processing

E : Lecture 1 Introduction

Responses of Digital Filters Chapter Intended Learning Outcomes:

Digital Signal Processing:

UNIVERSITY OF OSLO. Please make sure that your copy of the problem set is complete before you attempt to answer anything.

EECE 301 Signals & Systems Prof. Mark Fowler

Introduction to DSP Time Domain Representation of Signals and Systems

EE Homework 5 - Solutions

Digital Signal Processing I Final Exam Fall 2008 ECE Dec Cover Sheet

EE 313 Linear Signals & Systems (Fall 2018) Solution Set for Homework #7 on Infinite Impulse Response (IIR) Filters CORRECTED

Cosc 3451 Signals and Systems. What is a system? Systems Terminology and Properties of Systems

EE 3054: Signals, Systems, and Transforms Spring A causal discrete-time LTI system is described by the equation. y(n) = 1 4.

Signals and Systems. Spring Room 324, Geology Palace, ,

Control Systems Design

Analysis of Finite Wordlength Effects

Frequency-Domain C/S of LTI Systems

PS403 - Digital Signal processing

ECGR4124 Digital Signal Processing Midterm Spring 2010

ECE538 Final Exam Fall 2017 Digital Signal Processing I 14 December Cover Sheet

Z-Transform. x (n) Sampler

2. CONVOLUTION. Convolution sum. Response of d.t. LTI systems at a certain input signal

summable Necessary and sufficient for BIBO stability of an LTI system. Also see poles.

VALLIAMMAI ENGINEERING COLLEGE. SRM Nagar, Kattankulathur DEPARTMENT OF INFORMATION TECHNOLOGY. Academic Year

Discrete Time Fourier Transform

Discrete-Time Signals and Systems

Digital Signal Processing

Module 4 : Laplace and Z Transform Problem Set 4

Transcription:

Lecture 18: Stability ECE 401: Signal and Image Analysis University of Illinois 4/18/2017

1 Stability 2 Impulse Response 3 Z Transform

Outline 1 Stability 2 Impulse Response 3 Z Transform

BIBO Stability A system is BIBO Stable (bounded-input-bounded-output) if and only if every bounded input yields a bounded output. In other words, a system is stable if and only if: For EVERY x[n] such that there is some finite A such that x[n] A for every n, the corresponding y[n] satisfies y[n] B for every n, for some finite B.

Example of a Stable System Consider this system: y[n] = 10, 000x 2 [n] This system is stable because x[n] A y[n] 1000A 2 Since 1000A 2 is a finite number, the system is stable.

System Gain System Gain is defined to be the maximum possible ratio of output amplitude over input amplitude, as computed over all possible input signals: G = max x[n] B A For example, for the system y[n] = 1000x 2 [n], G = max x[n] 1000A 2 A = 1000A Key idea: an unstable system is one with infinite gain.

The Practical Use of Stability Practical situation: you might have a system that can only represent signals less than some maximum amplitude, for example fractional-arithmetic hardware requires Typical hardware requirements: x[n] 1, y[n] 1 If G is finite, then you can implement the system on fractional-arithmetic hardware as follows: x[n] = x[n] G, y[n] = system ( x[n])

Example of an Unstable System Consider this system: y[n] = x[n] u[n] This system is unstable. To prove it s unstable, we just need to find a bounded input (any bounded input) that generates an unbounded output. For example: x[n] = u[n], x[n] 1 Produces which is unbounded. y[n] = (n + 1)u[n]

Practical Consequences of Instability In practice, instability is hard to debug, because it looks like your system is producing silence as the output. For example, suppose you have: F s = 44, 100 samples/second 16 bits/sample, so the largest possible sample value is 2 15 = 32768 A system that generates y[n] = x[n] + y[n 1] The input x[n] = 1000u[n].... then in just 32 samples (less than one millisecond), the system will hit y[n] = 32768. After that, it will have y[n] = 32768, constant, forever. The system output will sound like perfect silence; you ll think your speakers have gone dead.

Outline 1 Stability 2 Impulse Response 3 Z Transform

Use the Impulse Response to Test Stability If the system is LTI, then you can use the impulse response to test for instability. y[n] = h[m]x[n m] m= Suppose we have the requirement x[n m] A. Then the biggest possible output sample is achieved, at sample y[n], if x[n m] = Asign (h[m]) Then y[n] = A h[m] m=

System Gain of an LTI System The system gain of an LTI system is G = m= h[m] This worst-case input-output gain is achieved if, for any value of n, it turns out that x[n m] = Asign (h[m])

Examples: Stable Systems All FIR systems are stable, as long as they have finite coefficients. Suppose h[n] C Suppose h[n] is only N samples long Then the system gain is G NC, which is finite, therefore the system is stable. Any IIR system with a decaying exponential impulse response is stable. Suppose h[n] = a n cos(ω 0 n)u[n]. Then h[n] < a n If a < 1, then m= h[n] < m=0 a n = 1 1 a

Examples: Unstable Systems h[m] = u[m] is unstable (worst-case input: x[n] = u[n]). h[m] = cos(ω 0 m)u[m] is unstable (worst-case input: x[n m] = sign (cos(ω 0 m)) Even an ideal lowpass filter is unstable!! sin(ω c n) πn = 2 π m= m= 1 πn =... and of course, h[n] = a n u[n] is unstable if a > 1.

Outline 1 Stability 2 Impulse Response 3 Z Transform

LCCDE Systems An LCCDE system has this transfer function: H(z) = M m=0 b mz m 1 N m=1 a mz m If M N, then it has a partial fraction expansion: H(z) = C 0 + N k=1 C k 1 p k z 1 So N h[n] = C 0 δ[n] + C k pk n u[n] k=1

LCCDE Systems An LCCDE system has this impulse response: h[n] = C 0 δ[n] + N C k pk n u[n] k=1 Each pole is a complex number,p k = a k e jθ k. There are three possibilities: 1 a k < 1. In this case, pk n is exponentially decaying, so this pole is stable. 2 a k > 1. In this case, pk n is exponentially increasing, so this pole is unstable. 3 a k = 1. In this case, p n k + pn k+1 = ejθ kn + e jθ kn = 2 cos(θ k n), which is unstable: an input at the same frequency will cause y[n] to be unbounded.

LCCDE System Stable IFF Poles Inside Unit Circle An LCCDE system has this impulse response: N h[n] = C 0 δ[n] + C k pk n u[n] k=1 This system is stable if and only if p k < 1 for all poles. We say that an LCCDE is stable if and only if all of the poles are inside the unit circle. Notice it doesn t matter where the zeros are. Stability only requires the poles to be inside the unit circle, not the zeros.