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ECE 352 Systems II
Manish K. Gupta, PhD Office: Caldwell Lab 278
Email: guptam @ ece. osu. edu Home Page: http://www.ece.osu.edu/~guptam
TA: Zengshi Chen Email: chen.905 @ osu. edu Office Hours for TA : in CL 391: Tu & Th 1:00-2:30 pm
Home Page: http://www.ece.osu.edu/~chenz/
Acknowledgements
• Various graphics used here has been taken from public resources instead of redrawing it. Thanks to those who have created it.
• Thanks to Brian L. Evans and Mr. Dogu Arifler
• Thanks to Randy Moses and Bradley Clymer
ECE 352
Slides edited from: • Prof. Brian L. Evans and Mr. Dogu Arifler
Dept. of Electrical and Computer Engineering The University of Texas at Austin course:
EE 313 Linear Systems and Signals Fall 2003
tfbtfdt
dbtf
dt
dbtf
dt
db
tyatydt
daty
dt
daty
dt
d
m
m
mm
m
m
n
n
nn
n
011
011
1
1
k
kk
dt
dD
dt
dD
dt
dD
2
22
tfbDbDbDbtyaDaDaDDP
mm
mm
DQ
nn
n
)(
011
1
)(
011
1
Continuous-Time Domain Analysis• Example: Differential systems
– There are n derivatives of y(t) and m derivatives of f(t)– Constants a0, a1, …, an-1 and b0, b1, …, bm
– Linear constant-coefficient differential equation
• Using short-hand notation,above equation becomes
m
l
ll
n
k
kk
DbbDP
DaaDQ
10
10
)(
)(
Continuous-Time Domain Analysis• Polynomials
Q(D) and P(D)where an = 1:
– Recall n derivatives of y(t) and m derivatives of f(t)– We will see that this differential system behaves as an
(m-n)th-order differentiator of high frequency signals if m > n
– Noise occupies both low and high frequencies– We will see that a differentiator amplifies high
frequencies. To avoid amplification of noise in high frequencies, we assume that m n
tyDQctfDPctfcDP
tfDPtyDQ
111111
tyDQctfcDP 2222
tyDQctyDQc
tfcDPtfcDPtfctfcDP
2211
22112211
tytyDQtftfDP 2121
Continuous-Time Domain Analysis• Linearity: for any complex constants c1 and c2,
)(on dependent
zero are conditions initial all
)( zero-non toresponse
)( oft independen
only conditions system internal from results
0)(when
response state-zero responseinput -zeroresponse Total
tf
tf
tf
tf
Continuous-Time Domain Analysis• For a linear system,
– The two components are independent of each other
– Each component can be computed independently of the other
T[·] y(t)f(t)
Continuous-Time Domain Analysis• Zero-input response
– Response when f(t)=0
– Results from internal system conditions only
– Independent of f(t)
– For most filtering applications (e.g. your stereo system), we want no zero-input response.
• Zero-state response– Response to non-zero f(t)
when system is relaxed
– A system in zero state cannot generate any response for zero input.
– Zero state corresponds to initial conditions being zero.
Zero-Input Response• Simplest case
• Solution:
• For arbitrary constant C– Could C be complex?
– How is C determined?
0000 tyatydt
d tyatydt
d000
taeCty 0 0
dt
dDtyDQ where00
00011
1 tyaDaDaD n
nn
tnn
nn
t
t
eCdt
ydtyD
eCdt
ydtyD
eCdt
dytDy
00
22
02
02
00
Zero-Input Response• General case:
• The linear combination of y0(t) and its n successive derivatives are zero.
• Assume that y0(t) = C e t
Zero-Input Response• Substituting into the differential equation
• y0(t) = C et is a solution provided that Q() = 0.
• Factor Q() to obtain n solutions:
Assuming that no two i terms are equal
0011
1
zeronon
t
Q
nn
n
zeronon
eaaaC
0)( 21 nQ
tn
tt neCeCeCty 21210
Zero-Input Response• Could i be complex? If complex,
• For conjugate symmetric roots, and conjugate symmetric constants,
iii j
termgoscillatin
termdampening
sincos tjte
eeee
iit
tjttjt
i
iiiii
termgoscillatin
1
termdampening
111 cos2 CteCeCeC ittt iii
Zero-Input Response• For repeated roots, the solution changes.• Simplest case of a root repeated twice:
• With r repeated roots
002 tyD
tetCCty 210
0 0 tyD r
trr etCtCCty 1
210
System Response• Characteristic equation
– Q(D)[y(t)] = 0
– The polynomial Q()• Characteristic of system
• Independent of the input
– Q() roots 1, 2, …, n
• Characteristic roots a.k.a. characteristic values, eigenvalues, natural frequencies
• Characteristic modes (or natural modes) are the time-domain responses corresponding to the characteristic roots– Determine zero-input
response
– Influence zero-state response
RLC Circuit• Component values
L = 1 H, R = 4 , C = 1/40 FRealistic breadboard components?
• Loop equations(D2 + 4 D + 40) [y0(t)] = 0
• Characteristic polynomial 2 + 4 + 40 =
( + 2 - j 6)( + 2 + j 6)
• Initial conditionsy(0) = 2 Aý(0) = 16.78 A/s
L R
C
y(t)
f(t)
Envelope
y0(t) = 4 e-2t cos(6t - /3) A
ECE 352
Linear Time-Invariant System
• Any linear time-invariant system (LTI) system, continuous-time or discrete-time, can be uniquely characterized by its– Impulse response: response of system to an impulse
– Frequency response: response of system to a complex exponential e j 2 f for all possible frequencies f
– Transfer function: Laplace transform of impulse response
• Given one of the three, we can find other two provided that they exist
ECE 352
Impulse response
Impulse response of a system is response of the system to an input that is a unit
impulse (i.e., a Dirac delta functional in continuous time)
ECE 352
Example Frequency Response
• System response to complex exponential e j for all possible frequencies where = 2 f
• Passes low frequencies, a.k.a. lowpass filter
|H()|
p ss p
passband
stopband stopband
H()
ECE 352
Kronecker Impulse
• Let [k] be a discrete-time impulse function, a.k.a. the Kronecker delta function:
• Impulse response h[k]: response of a discrete-time LTI system to a discrete impulse function
00
01
k
kk
k
[k]
1
Transfer Functions
Zero-State Response• Q(D) y(t) = P(D) f(t)• All initial conditions are 0 in zero-state response
• Laplace transform of differential equation, zero-state component
sYty
sFstfdt
dtfD
sYstydt
dtyD
kk
kk
rr
rr
input
response state zero
L
L
sQ
sP
sF
sYsH
sFsPsYsQ
sFtf
Transfer Function• H(s) is called the transfer function because it
describes how input is transferred to the output in a transform domain (s-domain in this case)Y(s) = H(s) F(s)
y(t) = L-1{H(s) F(s)} = h(t) * f(t) H(s) = L{h(t)}
• The transfer function is the Laplace transform of the impulse response
Transfer Function• Stability conditions for an LTIC system
– Asymptotically stable if and only if all the poles of H(s) are in left-hand plane (LHP). The poles may be repeated or non-repeated.
– Unstable if and only if either one or both of these conditions hold: (i) at least one pole of H(s) is in right-hand plane (RHP); (ii) repeated poles of H(s) are on the imaginary axis.
– Marginally stable if and only if there are no poles of H(s) in RHP, and some non-repeated poles are on the imaginary axis.
Ts
Ts
esF
sYsH
esFsY
Ttfty
Examples• Laplace transform
• Assume input f(t) & output y(t) are causal
• Ideal delay of T seconds
0
dtetfsF ts
ssF
sYsH
sFsfsFssY
dt
dfty
)(0
s
sH
ysFs
sY
dftyt
1
010
Examples• Ideal integrator with
y(0-) = 0
• Ideal differentiator with f(0-) = 0
Cascaded Systems• Assume input f(t) and output y(t) are causal
• Integrator first,then differentiator
• Differentiator first,then integrator
• Common transfer functions– A constant (finite impulse response)– A polynomial (finite impulse response)– Ratio of two polynomials (infinite impulse response)
1/s sF(s)/s
tdf
0f(t)
F(s)
f(t)
F(s)
s 1/ss F(s)
dt
dff(t)
F(s)
f(t)
F(s)
Frequency-Domain Interpretation
• y(t) = H(s) e s t
for a particular value of s
• Recall definition offrequency response:
sH
sts
ts
ts
dehe
deh
ethty
h(t) y(t)est
h(t) y(t)ej 2f t
fH
fjtfj
tfj
tfj
dehe
deh
ethty
2 2
2
2
Frequency-Domain Interpretation• s is generalized frequency: s = + j 2 f• We may convert transfer function into
frequency response by if and only if region of convergence of H(s) includes the imaginary axis
• What about h(t) = u(t)?We cannot convert this to a frequency response
However, this system has a frequency response
• What about h(t) = (t)?
0Refor 1
ss
sH
1 allfor 1 fHssH
fjs
sHfH2
Unilateral Laplace Transform• Differentiation in time property
f(t) = u(t)
What is f ’(0)? f’(t) = (t).f ’(0) is undefined.
By definition of differentiation
Right-hand limit, h = h = 0, f ’(0+) = 0
Left-hand limit, h = - h = 0, f ’(0-) does not exist
t
f(t)
1
h
tfhtftf
h
0lim
Block DiagramsH(s)F(s) Y(s)
H1(s) + H2(s)F(s) Y(s)
H1(s)
F(s) Y(s)
H2(s)
=
H1(s)F(s) Y(s)H2(s) H1(s)H2(s)F(s) Y(s)=W(s)
G(s) 1 + G(s)H(s)
F(s) Y(s)G(s)F(s) Y(s)
H(s)
-
=E(s)
Derivations• Cascade
W(s) = H1(s)F(s) Y(s) = H2(s)W(s)
Y(s) = H1(s)H2(s)F(s) Y(s)/F(s) = H1(s)H2(s)
One can switch the order of the cascade of two LTI systems if both LTI systems compute to exact precision
• Parallel CombinationY(s) = H1(s)F(s) + H2(s)F(s)
Y(s)/F(s) = H1(s) + H2(s)
H1(s)F(s) Y(s)H2(s) H2(s)F(s) Y(s)H1(s)
Derivations• Feedback System
• Combining these two equations
sEsGsY
sYsHsFsE
sF
sHsG
sGsY
sFsGsYsHsGsY
sYsHsFsGsY
1
• What happens if H(s) is a constant K?– Choice of K controls all
poles in the transfer function
– This will be a common LTI system in Intro. to Automatic Control Class (required for EE majors)
Stability
Stability• Many possible
definitions
• Two key issues for practical systems– System response to zero
input
– System response to non-zero but finite amplitude (bounded) input
• For zero-input response– If a system remains in a particular
state (or condition) indefinitely, then state is an equilibrium state of system
– System’s output due to nonzero initial conditions should approach 0 as t
– System’s output generated by initial conditions is made up of characteristic modes
Stability• Three cases for zero-input response
– A system is stable if and only if all characteristic modes 0 as t
– A system is unstable if and only if at least one of the characteristic modes grows without bound as t
– A system is marginally stable if and only if the zero-input response remains bounded (e.g. oscillates between lower and upper bounds) as t
Characteristic Modes• Distinct characteristic roots 1, 2, …, n
0Re if
0Re if
0Re if0
lim
10
λ
λe
λ
e
ecty
tjt
t
n
j
tj
j
– Where = + j in Cartesian form
– Units of are in radians/second
Stable Unstable
Im{}
Re{}
MarginallyStable
Left-handplane (LHP)
Right-handplane (RHP)
Characteristic Modes• Repeated roots
– For r repeated roots of value .
– For positive k
r
i
tii etcty
1
10
0Reif
0Reif
0Reif0
lim
tk
tet
• Decaying exponential decays faster thantk increases for any value of k– One can see this by using
the Taylor Series approximation for et about t = 0:
...6
1
2
11 3322 ttt
Stability Conditions• An LTIC system is asymptotically stable if and
only if all characteristic roots are in LHP. The roots may be simple (not repeated) or repeated.
• An LTIC system is unstable if and only if either one or both of the following conditions exist:(i) at least one root is in the right-hand plane (RHP)(ii) there are repeated roots on the imaginary axis.
• An LTIC system is marginally stable if and only if there are no roots in the RHP, and there are no repeated roots on imaginary axis.
dtfhdtfhty
dtfh
tfthty
dττhCty Ctf
tCtftf
and ,
then, i.e.bounded, is )( If
Response to Bounded Inputs• Stable system: a bounded input (in amplitude)
should give a bounded response (in amplitude)• Linear-time-invariant (LTI) system
• Bounded-Input Bounded-Output (BIBO) stable
h(t) y(t)f(t)
Impact of Characteristic Modes• Zero-input response consists of the system’s
characteristic modes• Stable system characteristic modes decay
exponentially and eventually vanish• If input has the form of a characteristic mode,
then the system will respond strongly• If input is very different from the characteristic
modes, then the response will be weak
tuee
AtuetuAe
tfthty
tt
tt
responseweak amplitude small
response strongamplitude large
resonance
tuet
ty
t
Impact of Characteristic Modes• Example: First-order system with characteristic
mode e t
• Three cases
tueRC
th RC
t
1
e-1/RC
1/RC
t
h(t)
System Time Constant• When an input is applied to a system, a certain
amount of time elapses before the system fully responds to that input– Time lag or response time is the system time constant
– No single mathematical definition for all cases
• Special case: RC filter– Time constant is = RC
– Instant of time at whichh(t) decays to e-1 0.367 of its maximum value
t0 tht
h(t)
h(t0) h(t)
ĥ(t)
RCt
dteRCRC
t
h
RC
t
h
0
11
System Time Constant• General case:
– Effective duration is th seconds where area under ĥ(t)
– C is an arbitrary constant between 0 and 1
– Choose th to satisfy this inequality
• General case appliedto RC time constant:
000
)( )( )(ˆ dtthCthtdtth h
th
h(t) y(t)u(t)
t
u(t)
1
t
h(t)
A
t
y(t)
A th
tr
Step Response• y(t) = h(t) * u(t)
• Here, tr is the rise time of the system• How does the rise time tr relate to the system
time constant of the impulse response?• A system generally does not respond to an input
instantaneously
tr
Filtering• A system cannot effectively respond to periodic
signals with periods shorter than th
• This is equivalent to a filter that passes frequencies from 0 to 1/th Hz and attenuates frequencies greater than 1/th Hz (lowpass filter)– 1/th is called the cutoff frequency– 1/tr is called the system’s bandwidth (tr = th)
• Bandwidth is the width of the band of positive frequencies that are passed “unchanged” from input to output
Transmission of Pulses• Transmission of pulses through a system (e.g.
communication channel) increases the pulse duration (a.k.a. spreading or dispersion)
• If the impulse response of the system has duration th and pulse had duration tp seconds, then the output will have duration th + tp
System Realization
Passive Circuit Elements• Laplace transforms with
zero-valued initial conditions
• Capacitor
• Inductor
• Resistor
sLsI
sVsH
sIsLsVdt
diLtv
sCsI
sVsH
sIsC
sV
sVsCsIdt
dvCti
1
1
RsI
sVsH
sIRsV
tiRtv
+
–
v(t)
+
–
v(t)
+
–
v(t)
First-Order RC Lowpass Filter
x(t) y(t)
++
C
R
X(s) Y(s)
++R
sC
1
Time domain
Laplace domainCR
s
CRsX
sY
sX
sCR
sCsY
sIsC
sY
sCR
sXsI
1
1
)(
)(
)(
1
1
)(
)(
1)(
1)(
)(
i(t)
I(s)
Passive Circuit Elements• Laplace transforms with
non-zero initial conditions
• Capacitor
s
isIsL
iLsIsL
isIsLsV
dt
diLtV
0
0
0
• Inductor
0
1
0
1
0
vCsIsC
s
vsI
sCsV
vsVsCsI
dt
dvCti
Operational Amplifier
• Ideal case: model this nonlinear circuit as linear and time-invariantInput impedance is extremely high (considered infinite)
vx(t) is very small (considered zero)
+
_
y(t)
+
_
+_vx(t)
Operational Amplifier Circuit
• Assuming that Vx(s) = 0,
• How to realize gain of –1?• How to realize gain of 10?
+
_
Y(s)
+
_
+_Vx(s)
Zf(s)
Z(s)
+_
F(s)
I(s)
sZ
sZ
sF
sYsH
sFsZ
sZsY
sZ
sFsI
sZsIsY
f
f
f
H(s)
Differentiator• A differentiator amplifies high frequencies, e.g.
high-frequency components of noise:H(s) = s where s = + j 2fFrequency response is H(f) = j 2 f | H( f ) |= 2 f |
• Noise has equal amounts of low and high frequencies up to a physical limit
• A differentiator may amplify noise to drown out a signal of interest
• In analog circuit design, one would use integrators instead of differentiators
Initial and Final Values• Values of f(t) as t 0 and t may be
computed from its Laplace transform F(s) • Initial value theorem
If f(t) and its derivative df/dt have Laplace transforms, then provided that the limit on the right-hand side of the equation exists.
• Final value theoremIf both f(t) and df/dt have Laplace transforms, then
provided that s F(s) has no poles in the RHP or on the imaginary axis.
ssFfs
lim0
ssFtfst 0limlim
52
32102
sss
ssY
0
521
32
10lim
52
3210lim
lim0
2
2
2
ss
ss
ss
s
ssYy
s
s
s
65
30
52
3210lim
limlim
20
0
ss
s
ssYty
s
st
Final and Initial Values Example
• Transfer functionPoles at s = 0, s = -1 j2
Zero at s = -3/2