Multivariable Control Systemskarimpor.profcms.um.ac.ir/imagesm/...limitation.pdf · Limitation on Performance in MIMO Systems ... Bode plot of L(jZ) ( ) 1 L jZ 180 GM L( jZ 180) 180

Dr. Ali Karimpour Jan 2017

Lecture 5

Multivariable Control

Systems

Ali Karimpour

Associate Professor

Ferdowsi University of Mashhad

Lecture 6

References are appeared in the last slide.


Lecture 5

2

Limitation on Performance in MIMO Systems

Topics to be covered include:

Scaling and Performance

Shaping Closed-loop Transfer Functions

Fundamental Limitation on Sensitivity

Limitations Imposed by RHP Zeros

Limitations Imposed by Unstable (RHP) Poles

Limitations Imposed by Time Delays

Fundamental Limitation on Performance (Frequency domain)

Fundamental Limitation on Performance (Time domain)

A Brief Review of Linear Control Systems


Lecture 5

3


Time Domain Performance

Frequency Domain Performance

Bandwidth and Crossover Frequency


Lecture 5

4


• Nominal stability NS: The system is stable with no model

uncertainty.

• Nominal Performance NP: The system satisfies the performance

specifications with no model uncertainty.

• Robust stability RS: The system is stable for all perturbed plants

about the nominal model up to the worst case model uncertainty.

• Robust performance RP: The system satisfies the performance

specifications for all perturbed plants about the nominal model up

to the worst case model uncertainty.


Lecture 5

5


The objective of this section is to discuss the ways of

evaluating closed loop performance.

Although closed loop stability is an important issue,

the real objective of control is to improve performance, that is,

to make the output y(t) behave in a more desirable manner.

Actually, the possibility of inducing instability is one of the

disadvantages of feedback control which has to be traded off

against performance improvement.


Lecture 5

6


Step response of a system

Rise time, tr , the time it takes for the output to first reach 90% of

its final value, which is usually required to be small.

Settling time, ts , the time after which the output remains within

5% ( or 2%) of its final value, which is usually required to be small.

Overshoot, P.O, the peak value divided by the final value, which

should typically be less than 20% or less.


Lecture 5

7



Decay ratio, the ratio of the second and first peaks, which should

typically be 0.3 or less.

Steady state offset, ess, the difference between the final value and

the desired final value, which is usually required to be small.


Lecture 5

8


Total variation(TV), it is the total movement of the output as shown.

i

ivTV


Excess variation, the total variation (TV) divided by the overall

change at steady state, which should be as close to 1 as possible.

0/ variationExcess vTV


Lecture 5

9


• IAE : Integral absolute error deIAE

0

)(

• ISE : Integral squared error deISE 2

0)(

• ITSE : Integral time weighted squared error deITSE 2

0)(

• ITAE : Integral time weighted absolute error deITSE )(0


Lecture 5

10


Let L(s) denote the loop transfer function of a system which is

closed-loop stable under negative feedback.

Bode plot of )( jL

)(

1

180jLGM

180)( 180 jL

1)( cjL

180)( cjLPM

cPM /max


Lecture 5

11


Let L(s) denote the loop transfer function of a system which is

closed-loop stable under negative feedback.

)(

1

180jLGM

180)( 180 jL

1)( cjL

180)( cjLPM

cPM /max

Nyquist plot of )( jL


Lecture 5

12


Stability margins are measures of how close a stable closed-loop system

is to instability.

From the above arguments we see that the GM and PM provide stability

margins for gain and delay uncertainty.

More generally, to maintain closed-loop stability, the Nyquist stability

condition tells us that the number of encirclements of the critical point

-1 by must not change. )( jL

Thus the actual closest distance to -1 is a measure of stability


Lecture 5

13


One degree-of-freedom

configuration

nGKIGKdGGKIrGKIGKsy d

111 )()()()(

Complementary sensitivity

function )(sTSensitivity

function )(sS

The maximum peaks of the sensitivity and complementary sensitivity

functions are defined as

)(max)(max

jTMjSM Ts


Lecture 5

14


11)()()()(

jLIjKjGIjS

)(max

jSM s

Thus one may also view Ms as a robustness measure.

sM

1


Lecture 5

15


There is a close relationship between MS and MT and the GM and PM.

][1

2

1sin2;

1

1 radMM

PMM

MGM

sss

s

][1

2

1sin2;

11 1 rad

MMPM

MGM

TTT

For example, with MS = 2 we are guaranteed GM > 2 and PM >29˚.

For example, with MT = 2 we are guaranteed GM > 1.5 and PM >29˚.


Lecture 5

16


configuration


111 )()()()(

Complementary sensitivity

function )(sTSensitivity

function )(sS

Trade-offs in Frequency Domain

)(sT

IsSsT )()(

TndSGSrrye d KSndKSGKSru d


Lecture 5

17

Trade-offs in Frequency Domain

IsSsT )()(

TndSGSrrye d KSndKSGKSru d

1)()(

sLIsS

• Performance, good disturbance rejection LITS or or 0

• Performance, good command following LITS or or 0

• Mitigation of measurement noise on output 0or or 0 LIST

• Small magnitude of input signals 0Lor 0or 0 TK

• Physical controller must be strictly proper 0Tor 0or 0 LK

• Nominal stability (stable plant) small be L

• Stabilization of unstable plant ITL or large be


Lecture 5

18


Definition 5-1

below. from db 3- crossesfirst )S(j

wherefrequency theis , ,bandwidth The B

Definition 5-2

above. from db 3- crosses )T(jat which

frequency highest theis , ,bandwidth The BT

Definition 5-3

above. from db 0 crosses )L(j where

frequency theis , ,frequency crossover gain The c

100

101

102

103

-60

-40

-20

0

20

40

60Singular Values

Frequency (rad/sec)

Sin

gula

r V

alu

es (

dB

)

L

T

S


Lecture 5

19


Specifically, for systems with PM < 90˚ (most practical systems) we have

BTcB

In conclusion ωB ( which is defined in terms of S ) and also ωc

( in terms of L ) are good indicators of closed-loop performance,

while ωBT ( in terms of T ) may be misleading in some cases.

100

101

102

103

-60

-40

-20

0

20

40

60Singular Values

Frequency (rad/sec)

Sin

gula

r V

alu

es (

dB

)

L

T

S


Lecture 5

20


Example 5-1 : Comparison of and as indicators of performance.BTB

1,1.0;1

1;

)2(Let

z

szs

zsT

zss

zsL

036.0B054.0c 1BT 1.0 zero RHPan is There z


Lecture 5

21


Example 5-1 : Comparison of and as indicators of performance.BTB

1,1.0;1

1;

)2(Let

z

szs

zsT

zss

zsL

036.0B054.0c 1BT 1.0 zero RHPan is There z


Lecture 5

22

Introduction


configuration


111 )()()()(

nsTdGsSrsTsy d )()()()(

• Performance, good disturbance rejection LITS or or 0

• Performance, good command following LSIT or 0or

• Mitigation of measurement noise on output 0or or 0 LIST


Lecture 5

23












Lecture 5

24

Scaling

ryedGuGy dˆˆˆ;ˆˆˆˆˆ

rDreDeyDyuDudDd eeeudˆ,ˆ,ˆ,ˆ,ˆ 11111

rDyDeDdDGuDGyDeeeddue

;

ddedue DGDGDGDG ˆ,ˆ 11 ryedGGuy d ;


Lecture 5

25


For any reference r(t) between -R and R and any disturbance d(t)

between -1 and 1, keep the output y(t) within the range r(t)-1 to r(t)+1

(at least most of the time), using an input u(t) within the range -1 to 1.

For any disturbance |d(ω)| ≤ 1 and any reference |r(ω)|≤ R(ω),

the performance requirement is to keep at each frequency ω the

control error |e(ω)|≤1 using an input |u(ω)|≤1.


Lecture 5

26


Many design procedure act on the shaping of the open-loop transfer

function L.

An alternative design strategy is to directly shape the magnitudes of

closed-loop transfer functions, such as S(s) and T(s).

Such a design strategy can be formulated as an H∞ optimal control

problem, thus automating the actual controller design and leaving

the engineer with the task of selecting reasonable bounds “weights”

on the desired closed-loop transfer functions.


Lecture 5

27

The terms H∞ and H2

The H∞ norm of a stable transfer function matrix F(s) is simply define

as,

)(max)(

jFsF

We are simply talking about a design method which aims to press down

the peak(s) of one or more selected transfer functions.

Now, the term H∞ which is purely mathematical, has now established

itself in the control community.

In literature the symbol H∞ stands for the transfer function matrices

with bounded H∞-norm which is the set of stable and proper transfer

function matrices.


Lecture 5

28

The terms H∞ and H2

The H2 norm of a stable transfer function matrix F(s) is simply define

as,

djFjFtrsF H)()(

2

1)(

2

Similarly, the symbol H2 stands for the transfer function matrices with

bounded H2-norm, which is the set of stable and strictly proper transfer

function matrices.

Note that the H2 norm of a semi-proper transfer function is infinite,

whereas its H∞ norm is finite.

Why?


Lecture 5

29

Weighted Sensitivity

As already discussed, the sensitivity function S is a very good indicator

of closed-loop performance (both for SISO and MIMO systems).

The main advantage of considering S is that because we ideally want

S to be small, it is sufficient to consider just its magnitude, ||S|| that is,

we need not worry about its phase.

Why S is a very good indicator of closed-loop performance in many

literatures?


Lecture 5

30


Typical specifications in terms of S include:

• Minimum bandwidth frequency ωB*.

• Maximum tracking error at selected frequencies.

• System type, or alternatively the maximum steady-state tracking

error, A.

• Shape of S over selected frequency ranges.

• Maximum peak magnitude of S, ||S(jω)||∞≤M

The peak specification prevents amplification of noise at high frequencies,

and also introduces a margin of robustness; typically we select M=2


Lecture 5

31


)(

1

swp

,)(

1)(

jwjS

P

Mathematically, these specifications may be captured simply by an

upper bound

1)()(,1)()(

jSjwjSjw PP

The subscript P stands for performance


Lecture 5

32

Weight Selection

As

Mssw

B

BP

/)(

plot of )(

1

jwP

Performance at Low Frequencies


Lecture 5

33

Weight Selection

Performance at High Frequencies

plot of )(

1

jwP

B

P

s

Msw

1)(


Lecture 5

34

Weight Selection

A weight which asks for a slope -2 for L at lower frequencies is

22/1

22/1/

)(As

Mssw

B

BP

The insight gained from the previous section on loop-shaping design

is very useful for selecting weights.

For example, for disturbance rejection

1)( jSGd

It then follows that a good initial choice for the performance weight is

to let wP(s) look like |Gd(jω)| at frequencies where |Gd(jω)| >1


Lecture 5

35



Lecture 5

36

Stacked Requirements: Mixed Sensitivity

The specification ||wPS||∞<1 puts a lower bound on the bandwidth,

but not an upper one, and nor does it allow us to specify the roll-off

of L(s) above the bandwidth.

To do this one can make demands on another closed-loop transfer

function

KSw

Tw

Sw

NjNN

u

T

P

,1)(max

For SISO systems, N is a vector and

222)( KSwTwSwN uTP


Lecture 5

37

Stacked Requirements: Mixed Sensitivity

The stacking procedure is selected for mathematical convenience as

it does not allow us to exactly specify the bounds.

For example, TwKSwK TP )()( Let 21

We want to achieve

(I) 1 and 1 21

This is similar to, but not quite the same as the stacked requirement

(II)12

2

2

1

2

1

707.0 and 707.0 toLeads (II) If 2121

In general, with n stacked requirements the resulting error is at most n


Lecture 5

38

Solving H∞ Optimal Control Problem

After selecting the form of N and the weights, the H∞ optimal controller

is obtained by solving the problem

)(min KN

K

Let denote the optimal H∞ norm. )(min0 KN

K

The practical implication is that, except for at most a factor the

transfer functions will be close to times the bounds selected by

the designer.

n

0

This gives the designer a mechanism for directly shaping the magnitudes of

)( and )( , )( KSTS


Lecture 5

39


Example 5-2110

100)(,

)105.0(

1

110

200)(

2

ssG

sssG d

The control objectives are:

1. Command tracking: The rise time (to reach 90% of the final value)

should be less than 0.3 second and the overshoot should be less than 5%.

2. Disturbance rejection: The output in response to a unit step disturbance

should remain within the range [-1,1] at all times, and it should return to 0

as quickly as possible (|y(t)| should at least be less than 0.1 after 3 seconds).

3. Input constraints: u(t) should remain within the range [-1,1] at all times

to avoid input saturation (this is easily satisfied for most designs).


Lecture 5

40


Consider an H∞ mixed sensitivity S/KS design in which

KSw

SwN

u

P

It was stated earlier that appropriate scaling has been performed so that

the inputs should be about 1 or less in magnitude, and we therefore

As

Mssww

B

BPu

/)( and 1


Lecture 5

41


110

100)(of diagram Bode theSee

ssGd

-20

-10

0

10

20

30

40

Magnitu

de (

dB

)

10-3

10-2

10-1

100

101

102

-90

-45

0

Phase (

deg)

Bode Diagram

Frequency (rad/sec)

We need control till 10 rad/sec to reduce disturbance and a suitable rise time.

sec/10 let So radcB

Overshoot should be less than 5% so let MS<1.5


Lecture 5

42


410,10,5.1,/

)(

AM

As

Mssw B

B

BP

For this problem, we achieved an optimal H∞ norm of 1.37, so the

weighted sensitivity requirements are not quite satisfied. Nevertheless,

the design seems good with

rad/sec 22.5 and 2.71,04.8,0.1,30.1 cTS PMGMMM


Lecture 5

43


The tracking response is very good as shown by curve in Figure.

However, we see that the disturbance response is very sluggish.


Lecture 5

44


If disturbance rejection is the main concern, then from our earlier

discussion we need for a performance weight that specifies higher

gains at low frequencies. We therefore try

6

22/1

22/1

10,10,5.1,/

)(

AM

As

Mssw B

B

BP

For this problem, we achieved an optimal H∞ norm of 2.21, so the

weighted sensitivity requirements are not quite satisfied. Nevertheless,

the design seems good with

rad/sec 2.11 and 3.43,76.4,43.1,63.1 c PMGMMM TS


Lecture 5

45



Lecture 5

46












Lecture 5

47


(Frequency domain)

S plus T is the identity matrix

ITS

1)()(1)( STS

1)()(1)( TST


Lecture 5

48

Interpolation ConstraintsRHP-zero:

If G(s) has a RHP-zero at z with output direction yz then for internal stability

of the feedback system the following interpolation constraints must apply:

In MIMO Case:H

z

H

z

H

z yzSyzTy )(;0)(

In SISO Case: 1)(;0)( zSzT

0)( zGyH

z

Proof:

0)( zLyH

z LST

0)( zTyH

z0))(( zSIy

H

z

S has no RHP-pole


(Frequency domain)


Lecture 5

49


Moving the Effect of a RHP-zero to a Specific Output

Example 5-3

221

11

)1)(12.0(

1)(

ssssG

which has a RHP-zero at s = z = 0.5

The output zero direction is

45.0

89.0

1

2

5

1zy

Interpolation constraint is

0)()(2;0)()(2 22122111 ztztztzt


Lecture 5

50


Moving the Effect of a RHP-zero to a Specific Output

0)()(2;0)()(2 22122111 ztztztzt

zs

zszs

zs

sT

0

0)(0

???01

)( 11

zs

zs

zs

ssT 41

45.0

89.0zy

???

10

)(2

2

zs

s

zs

zs

sT

12


Lecture 5

51

Limitations Imposed by RHP Zero

Theorem 5-1 Assume that G(s) is square, functionally controllable and stable

and has a single RHP-zero at s = z and no RHP-pole at s = z. Then if the k’th

Element of the output zero direction is non-zero, i.e. yzk ≠ 0 it is possible to

obtain “perfect” control on all outputs j ≠ k with the remaining output

exhibiting no steady-state offset. Specifically, T can be chosen of the form

1...000...00

............

............

......

............

............

0...000...10

0...000...01

)(1121

zs

s

zs

s

zs

zs

zs

s

zs

s

zs

ssTnkk kjfor

y

y

zk

zj

j 2


Lecture 5

52

Interpolation ConstraintsRHP-pole:

If G(s) has a RHP pole at p with output direction yp then for internal

stability the following interpolation constraints apply

In MIMO Case: ppp yypTypS )(;0)(

In SISO Case: 1)(;0)( pTpS

Proof:

0)(1

pypL SLT T has no RHP-pole S has a RHP-zero

1TLS 0)()()( 1

pp ypSypLpT ppp yypSIypT )()(


(Frequency domain)


Lecture 5

53

Sensitivity Integrals

pN

i

ipdjS1

0)Re(.)(ln

If L(s) has two more poles than zeros (the loop transfer function L(s) of a

feedback system goes to zero faster than 1/s as s → ∞),(Bode integral)


(Frequency domain)

In SISO Case:

Figures are derived from:

“Feedback Systems” Karl Johan Astrom, Richard M. Murray, Princeton university press 2009


Lecture 5

54

Sensitivity Integrals

pN

i

i

i

i pdjSdjS1

00)Re(.)(ln)(detln

pN

i

ipdjS1

0)Re(.)(ln

If L(s) has two more poles than zeros (the loop transfer function L(s) of a

feedback system goes to zero faster than 1/s as s → ∞),(Bode integral)

In MIMO Case: (Generalization of SISO case)


(Frequency domain)

In SISO Case:


Lecture 5

55

Fundamental Limitation: Bounds on Peaks

TMSM TS min,min min,min,

In the following, MS,min and MT,min denote the lowest achievable values

for ||S||∞ and ||T||∞ , respectively, using any stabilizing controller K.


Lecture 5

56


Theorem 5-2 Sensitivity and Complementary Sensitivity Peaks

Consider a rational plant G(s) (with no time delay). Suppose G(s) has Nz

RHP-zeros with output zero direction vectors yz,i and Np RHP-poles with

output pole direction vectors yp,i. Suppose all zi and pi are distinct.

Then we have the following tight lower bound on ||T||∞ and ||S||∞

2/12/12

min,min, 1

pzpzTS QQQMM

ji

jp

H

iz

ijzp

ji

jp

H

ip

ijp

ji

jz

H

iz

ijzpz

yyQ

pp

yyQ

zz

yyQ

,,,,,,,,


Lecture 5

57


Example 5-4

2)1)(2(

)3)(1()(

ss

sssG

Derive lower bounds on ||T||∞ and ||S||∞

2,3,1 121 pzz

1

1,4/1,

6/14/1

4/12/1, pzpz QQQ

156786.12

9531.71 2

min,min,

TS MM


Lecture 5

58


2/12/12

min,min, 1

pzpzTS QQQMM

ji

jp

H

iz

ijzp

ji

jp

H

ip

ijp

ji

jz

H

iz

ijzpz

yyQ

pp

yyQ

zz

yyQ

,,,,,,,,

One RHP-pole and one RHP-zero

2

2

2

2

min,min, cossinpz

pzMM TS

p

H

z yy1cos

Exercise5-1 : Proof above equation.


Lecture 5

59


Example 5-5

3,2;

11.0

20

011.0

cossin

sincos

3

10

01

)(

pz

s

ss

zs

s

pssG

U

)3)(11.0(

20

0))(11.0(

)(,10

0100

ss

s

pss

zs

sGandU

0)3)(11.0(

))(11.0(

20

)(,01

109090

ss

zs

pss

s

sGandU


Lecture 5

60


5.0,2,/

,

BB

Pu MIs

MsWIW


Lecture 5

61


The corresponding responses to a step change in the reference r = [ 1 -1 ] , are shown

Solid line: y1

Dashed line: y2

1- For α = 0 there is one RHP-pole and zero so the responses for y1 is very poor.

2- For α = 90 the RHP-pole and zero do not interact but y2 has an undershoot since of …

3- For α = 0 and 30 the H∞ controller is unstable since of …


Lecture 5

62


The limitations of a RHP-zero located at z may also be derived from

the bound (by maximum module theorem)

)())((.)(max)()( zwjSjwsSsw PPP

1)()(

sSswP1)( zwP


Lecture 5

63


Performance at Low Frequencies 1)()(

sSswP

1)( zwP

As

Mssw

B

BP

/)( 1

/)(

Az

Mzzw

B

BP

Real zero: )(2

Iz

B

Imaginary zero

)1

1()1(M

zAB

)(87.0 IIzB

2

11

MzB

Exercise5-2 : Derive (I) and (II).


Lecture 5

64


Performance at High Frequencies 1)()(

sSswP

1)( zwP

11

)(

B

P

z

Mzw

Real zero: zB 2

B

P

s

Msw

1)(

MzB

/11

1

B

P

s

Msw

1)(


Lecture 5

65


)())((.)(max)()( pwjTjwsTsw TTT

1)()(

sTswT1)( pwT

TBT

TM

ssw

1)(

Real RHP-pole

1

T

TBT

M

Mp pBT 2

Imaginary RHP-pole pBT 15.1

pc 2


Lecture 5

66


ijj

i minmin

A lower bound on the time delay for output i is given by the smallest

delay in row i of G(s)

For MIMO systems we have the surprising result that an increased time

delay may sometimes improve the achievable performance. As a simple

example, consider the plant

1

11)(

sesG


Lecture 5

67











Lecture 5

68

Fundamental Limitation on Performance

(Time domain)

Reference: “Interaction Bounds in Multivariable Control Systems”

K H Johanson, Automatica, vol 38,pp 1045-1051, 2002

Consider the system:

)()()()(

)()()(

sYsRsCsU

sUsGsY

Let a step response signal at

i th input but other inputs are zero so

)(ˆ tur


Lecture 5

69


(Time domain)

Settling time is defined as:

ttrtyt kk

mksi ,)()(:infmax

0,...,1

ε is settling level.

0),()(sup0

trtyy iit

o

i

Undershoot is

defined as: 0),(sup

0

tyy it

u

i

The interaction from

ri to output k is: )(supˆ

0

tyy kt

ki

Overshoot in

output i is:

Rise time is defined as:

),0(,/ˆ)(:sup0

ttrtyt iri

Let a step response signal at i th input but other inputs are zero so )(ˆ tur


Lecture 5

70


(Time domain)

0)( zTyH

z

Remember that if open loop transfer function G has a real RHP zero z > 0 with

zero direction yz then we have:

Also we have

)(0 zTyH

z )()()(0 zYyzRzTyH

z

H

z

0)( dttyey ztH

z

Theorem5-3: Consider the stable closed loop system with zero initial conditions at

t=0 and let for t>0. Assume that the open loop transfer function

G has a real RHP zero z > 0 with zero direction yz and yz1 >0. then we have:

Trtr 0,...,0,ˆ)(

m

k

zkzzt

m

k

kzk

u

z yrye

yyyys

2

1

2

111 )ˆ(1

1ˆ

1

undershoot

interaction settling time settling levelelements of

zero direction


Lecture 5

71


(Time domain)


t=0 and let for t>0. Assume that the open loop transfer function

G has a real RHP zero z > 0 with zero direction yz and yz1 >0. then we have:

Trtr 0,...,0,ˆ)(

Remark1: For small settling level:

m

k

zkzzt

m

k

kzk

u

z yrye

yyyys

2

1

2

111 )ˆ(1

1ˆ

1

1

ˆˆ

1

1

2

111

szt

zm

k

kzk

u

ze

yryyyy

• For small zero lower bound is large.

• There is compromise between undershoot and interactions.

Remark2: For SISO system there is always undershoot but in MIMO systems,

however, we see that there is undershoot only if all but one element of zero direction

are zero.


Lecture 5

72


(Time domain)

Remember that if open loop transfer function G has a real RHP pole p > 0 with

pole direction yp then we have:

Let

sr

sr

sr

sR

/ˆ...00

0.../ˆ0

0...0/ˆ

)(

0)( pypS

and

)()()()( sSRsYsRsE

so we have:

pypRpS )()(0 pypS )(0

p

pt

p ydtteeypE .)()(0


Lecture 5

73


(Time domain)


t=0. Assume that the open loop transfer function G has a real RHP pole p > 0 with

pole direction yp and yp1 >0. Consider m independent responses with for

t>0. Then we have:

rtriˆ)(

p

pt

p ydtteeypE .)()(0

m

k

kpk

pt

pr

m

k

kpk

o

p yyeyptr

yyyy r

2

111

2

111ˆ1

2

ˆˆ 1

overshoot

interaction rise timeelements of

pole direction

Remark1: If the pole direction is such that

mkyy pkp ,...,2for 1

Then a real RHP pole far from the origin must necessarily give large overshoot if

the rise time is long.


Lecture 5

74


(Time domain)

Example5-6: Experimental set-up for the quadruple-tank process.


Lecture 5

75


(Time domain)

Example5-6(Continue): Experimental set-up for the quadruple-tank process.

pointset valve:1

Input#1

Pump1

Input#2

Pump2

Output#1 Output#2

pointset valve:2

Valve set points are used to make the

process more or less difficult to control.

6.0,7.0 here zero, RHP no 2,1 If 2121

1

11

sss

ssssG

67.981

24.3

)67.981)(90.381(

71.1

)57.951)(05.321(

04.2

57.951

11.3

)(

045.0012.0 21 zz

34.0,43.0 here zero, RHP one 1,0 If 2121

sss

ssssG

55.1111

97.1

)55.1111)(3.561(

11.3

)75.761)(30.521(

33.3

75.761

69.1

)(

051.0014.0 21 zz


Lecture 5

76


(Time domain)

High undershoot for small interaction.

For a unit step in r1 we have:

For a settling time of ts1=100 we have:


Lecture 5

77


(Time domain)


Lecture 5

78


(Time domain)


Lecture 5

Exercises

79

5-3 Consider the following weight with f>1.

5-4 Consider the weight

5-1 Mentioned in the lecture.

5-2 Mentioned in the lecture.


Lecture 5

Exercises (Continue)

80

5-5 Consider the plant

5-6 Repeat 5-5 for following plant.


Lecture 5

81

References

• Control Configuration Selection in Multivariable Plants, A. Khaki-Sedigh, B. Moaveni, Springer Verlag, 2009.

References

• Multivariable Feedback Control, S.Skogestad, I. Postlethwaite, Wiley,2005.

• Multivariable Feedback Design, J M Maciejowski, Wesley,1989.

• http://saba.kntu.ac.ir/eecd/khakisedigh/Courses/mv/

Web References

• http://www.um.ac.ir/~karimpor

• تحليل و طراحی سيستم های چند متغيره، دکتر علی خاکی صديق

Documents

Multivariable Control Systemskarimpor.profcms.um.ac.ir/imagesm/...limitation.pdf · Limitation on Performance in MIMO Systems ... Bode plot of L(jZ) ( ) 1 L jZ 180 GM L( jZ 180) 180