when is a linear system easy or difficult to control in ...helton/mtnshistory/contents/200… ·...
TRANSCRIPT
Shinji HaraThe University of Tokyo, Japan
When is a Linear System When is a Linear System Easy or Difficult Easy or Difficult
to Control in Practice ?to Control in Practice ?
MTNS’06, Kyoto (July, 2006)
Outline・・ Motivation & Background:Motivation & Background:・・ H2 Tracking Performance Limits:H2 Tracking Performance Limits:
new paradigm
Explicit analytical solutions with examples
・・ Concluding remarksConcluding remarks
Explicit analytical solutions with examples・ H2 Regulation Performance Limits:
・・ Phase Property vs Achievable Robustness Performance
H_inf loop shaping procedure -
Begin with ...
Motivation &Background
-New paradigm on -Control theory -
New Paradigm on Control TheoryNew Paradigm on Control Theory
Finde(t) y(t)r(t)
d(t)
GivenP(s)
GivenP(s)-
u(t)BestK(s)
e(t) y(t)r(t)
d(t)
-BestK(s)BestK(s)
u(t) Desirable
P(s)
Characterize
Assumption: L(s)=P(s)K(s): stable, r.d. >1
Bode Integral RelationBode Integral Relation
0|))(log(|0
=∫∞
ωω djSClosed-loop system: stable
ω
|)(| ωjS
∑ ipπ
)()(11:)(
sKsPsS
+=
・・ Question !Question !Is any stable & MP plant always easy to controlunder physical constraints in practice ?
Characterization of easily controllable plants in practical situations
・・ Aim of researches on control Aim of researches on control perfperf. limits:. limits:
control input energymeasurement accuracy
sampling periodchannel capacity etc.
Answer: NO !Answer: NO !
3-Disk Torsion System
All 3 TFs are marginally stable & MP, but the achievable performances are different.
1k
2k
1J
2J
3J
1c
2c
3c
�����
�����
�����
�����
T
1θ
3θ
2θ
3J
2J
1θ
3θ
2θ
1c
3c
2c
1k
2k
u
1J
-2.5 -2 -1.5 -1 -0.5 0-80
-60
-40
-20
0
20
40
60
80
Re
Im
極disk1の零点disk2の零点
Disk 1 poles
Disk 2
0 0.2 0.4 0.6 0.8 1-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1Step Response
Time (sec)
Am
plitude
(a)(b)
0 0.2 0.4 0.6 0.8 1-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1Step Response
Time (sec)A
mpl
itude
(a)(b)
Disk1 is better than Disk2. Why ?
Disk1 Disk2
Step responses
・・ Question !Question !Is any stable & MP plant always easy to controlunder physical constraints in practice ?
Characterization of easily controllable plants in practical situations
・・ To provide guidelines of plant design To provide guidelines of plant design from the view point of controlfrom the view point of control
・ From Controller Design to Plant Design
・・ Aim of researches on control Aim of researches on control perfperf. limits:. limits:
New ParadigmNew Paradigm
First topic ...
H2 Tracking Performance Limits
- explicit analytical solutions& applications -
“Best Tracking and Regulation Performance under Control Energy Constraint” by J. Chen, S. Hara & G. Chen, IEEE TAC (2003)
“Optimal Tracking Performance for SIMO Feedback Control Systems: Analytical closed-form expressions and guaranteed accuracy computation
” by S. Hara, M. Kanno & T. Bakhtiar, CDC’06 (submitted)
Control Performance LimitationsControl Performance Limitations
・ Time-response performance・ Tracking performance (H2 norm)
・ H-inf norm performance
・ Bode Integral Relation・ SISO stable/unstable・MIMO ・ Discrete-time/Sampled-data・ Nonlinear
・ Regulation performance (H2 norm)
Special issue in IEEE TAC, Aug. ,2003 Seron et. al. “Fundamental Limitations in Filtering and Control “
HH22 Optimal Tracking ProblemOptimal Tracking Problem
Performance Index:
control effort
unit step input
tracking error
SIMO plant
G(s)w(t)
u(t)y(t)
z(t)
P(s)P(s)
K(s)K(s)
1/s
-
−
−=
)(
)(
/10/1
)(sP
WsP
s
ssG u
Analytic solution
(closed-form)
Riccati & LMI
X1
uW
e(t)
SISO marginally stable plantSISO marginally stable plant
NMP zeros
Plant gain
11 =z
11 −=z1.0
2
*J
)( 21=uW
a
Numerical ExampleNumerical Example
Application to 3Application to 3--disk torsion systemdisk torsion system
uW
Disk 2Disk 3
Disk 1
J*
DiscreteDiscrete--time casetime case
NMP zeros
Plant gain
Delta Operator
Continuous-time result
General SIMO Case General SIMO Case Numerator:
Unstable poles & NMP zeros:
Stable terms:NMP zeros
Plant gain
Unstable terms: Unstable poles
Unstable pole / NMP zeros
RemarksRemarks::Several cases where the computation of
・ SIMO marginally stable ・ SISO non control input penalty・ SIMO
・ SIMO unstable: common unstable poles:Jcu=0many applications
is not required.
Optimal length of Inv. Pend. ?
0 0.5 1 1.5 23
3.5
4
4.5
5
5.5
6
l (m)
J* c2
Tracking performance limit
DiscreteDiscrete--time casetime case
NMP zeros
Plant gain
Delta Operator
Continuous-time result
Second topic ...H2 Regulation
Performance Limits-explicit analytical solutions
& an application -
“H2 Regulation Performance Limits for SIMO Feedback Control Systems” by T.Bakhtiar & S.Hara, MTNS’06
“Best Tracking and Regulation Performance under Control Energy Constraint” by J.Chen, S.Hara & G.Chen, IEEE TAC (2003)
HH22 Optimal Regulation ProblemOptimal Regulation Problem
Performance Index :
Impulseinput
SIMO plant
control effort performance on disturbance rejection
SISO MP plantSISO MP plant
unstable poles
Plant gain
Numerical ExampleNumerical Example
−1 0 1 2 3 4 50
500
1000
1500
2000
2500
3000
3500
p
Ec*
via Theorem 1via Toolbox
p
*cE
SIMO NMP plantSIMO NMP plant
MP case
CommonNMP zeros
Application to Application to a Magnetic Bearing Systema Magnetic Bearing System
Normalized state-space equation:
one unstable pole at p・current sensor:
・position sensor:
・multiple sensors:
NMPNMP
MPMP
MPMP
SISO MP discreteSISO MP discrete--time planttime plant: : r.d.=1
Delta Operator
Continuous-time result
Magnetic bearing systemMagnetic bearing system::
caused by discretizedNMP zeros
Last topic ...
Phase Property vs Achievable Robustness Performance
- H_inf loop shaping procedure -
“Finite Frequency Phase Property Versus Achievable Control Performance in H_inf Loop Shaping Design” by S. Hara, M. Kanno & M. Onishi,
SICE-ICCAS’06 (to be presented)
“Dynamical System Design from a Control Perspective: Finite frequency positive-realness approach” by T. Iwasaki & S. Hara, IEEE TAC (2003)
FFPRFFPR (Finite Frequency Positive Realness)
ー Finite Frequency Positive Realness ー
+<
−
DDBB
CIA
CIA
TT
T 00 0
XY
Y 20ω
X
,)(
= DC
BAsG
(LMI condition)
≤>+ ∀ || ,0)()( * ωωω jGjG 0ω
00 >ωgiven
..,0 tsYYXX TT =>=∃
HHinfinf LSDPLSDP(Hinf Loop-Shaping Design Procedure)
Good Phase Property
2nd order plant
Characterization of good plants
10-2
10-1
100
101
102
103
-60
-40
-20
0
20
40
60Bode Diagram
Magnitude (dB)
10-2
10-1
100
101
102
103
-270
-180
-90
0
90
Frequency (rad/sec)
Phase (deg)
-1.5 -1 -0.5 0 0.5 1 1.5-1.5
-1
-0.5
0
0.5
1
1.5Nyquist Diagram
Real Axis
Imaginary Axis
Numerical ExampleNumerical ExampleP(s)
L(s)=P(s)K(s)K(s)
Nyquist plotsBode diagrams
Concluding RemarksConcluding Remarks
・・ H2 tracking performance limitsH2 tracking performance limitsExplicit analytical solutions for
・・ H2 regulation performance limitsH2 regulation performance limits
Characterizations of easily controllable plants in practical situations, which provide guidelines of plant design from the view point of control
Finite frequency phase property vs achievable robustness performance in H_inf LSDP