designs of single neuron control systems: survey ~陳奇中教授演講投影片
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Direct Adaptive Process Control Based on Using a Single Neuron Controller:
Survey and Some New Results
陳奇中
Chyi-Tsong Chenctchen@fcu.edu.tw
Department of Chemical Engineering Feng Chia UniversityTaichung 407, Taiwan
逢甲大學化工系FCU PSE Lab., C.T. Chen
2
OutlineIntroduction
The single neuron controller (SNC) and its parameter tuning algorithm
Direct adaptive control schemes for chemical processes using SNCs
Some alternative SNC controllers and their parameter tuning algorithms
Model-based design of SNC control systems
Conclusions
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Conventional control strategies and limitationsStructure and design methodologies─ Open-loop control
─ Manual control─ Suitable for process whose mathematical model is hard to
characterize precisely
Introduction
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Closed-loop control system
─ Use system output error to generate control signal ─ Automatic control─ Widely used algorithm: PID type controller
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⎥⎦
⎤⎢⎣
⎡++= ∫
tD
Ic dt
tdedttetektu 0)()(1)()( τ
τ
⎥⎦
⎤⎢⎣
⎡−−++= ∑
=))1()(()()()(
0keke
TieTkekku
s
Dk
iI
sc
ττ
: proportional gain: integral time constant: derivative time constant: sampling time
ckIτDτST
PID controller for continuous system
PID controller for discrete system
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New challenges─ Extremely nonlinearities─ Immeasurable disturbances and uncertainties ─ Unknown or imprecisely known dynamics─ Time-varying parameters─ Multi-objectives
Modeling problem
─ Controller parameter's tuning problem ─ Control performance degradation
Artificial Intelligence (AI)
Motivation: Searching for new approaches for complex process control
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Research fields of AI
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Structure of neurons
An artificial neuron
Introduction to artificial neural networks
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Multilayer feedforward neural network
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receive signals from external environment
Signal transmission
transmit output signals to environment
10
Operations of an artificial neural network
1. Training or learning phase─ use input-output data to update the network parameters
(interconnection weights and thresholds)
2. Recall phase─ given an input to the trained network and then generate an
output
3. Generalization (prediction) phase─ given a new (unknown) input to the trained network and then
gives a prediction
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Properties (advantages) of MNN1. It has the ability of approximating any extremely
nonlinear functions.
2. It can adapt and learn the dynamic behavior under uncertainties and disturbances.
3. It has the ability of fault tolerance since the quantity and quality information are distributively stored in the weights and thresholds between neurons.
4. It is suitable to operate in a massive parallel framework.
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+
What happen when some neurons of the neural network werebroken down?
Direct adaptive control using a shape-tunable neural network controller (Chen and Chang, 1996)
single neuron controller
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The single neuron controller (SNC) and its parameter tuning algorithm
Single neuron controller
)(te )()()( tytyte d −=p Tba ],,[ θ≡pabθ
process output error, given bycontroller parameter vector, defined ascontrol output levelslope (sensitivity factor) bias
( ) ( ) [ ]{ }[ ])(exp1
)(exp1,θθ
−−+−−−
==ebebaeNLtu p
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θ−ee u
14
The characteristic plots for parameter a
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The characteristic plots for parameter b
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The characteristic plots for parameter θ
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A SNC-based direct adaptive control scheme
kee ku uydy +
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SNC parameters tuning algorithm
─ System performance
─ Parameter tuning algorithm (Chen, 2001)
where
and( ) ( ))(,)()()()()()( kkukukykkyk ppz Φ∂∂=∂∂≡
2))((21)( kyykE d −=
)()(1)()()()1(
kkkkekk T zz
zpp+
+=+ η
( )
( ) ⎥⎦
⎤⎢⎣
⎡⎟⎠⎞
⎜⎝⎛ +⎟⎠⎞
⎜⎝⎛ −−⎟
⎠⎞
⎜⎝⎛ +⎟⎠⎞
⎜⎝⎛ −−=
∂∂≡Φ
au
auab
au
auea
au
uu
1121,11
21,
,
θ
pp
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Stability of the SNC parameter learning algorithm
Assume is bounded
Let ;
the controller parameter vector converges to its local optimal asymptotically, where (the desired control input) and .
For the theoretical and rigorous proof, please refer to Chen (2001).
)(kz
20 <<η
p*p ( ) duNL =∗p,0
0*)( =pe
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A simplified version of the learning algorithm--- Using system response direction
)()(1)()()()1(
kkkkekk T zz
zpp+
+=+ η
parameter tuning algorithm (Chen, 2001)
where
( ) ( ))(,)()()()()()( kkukukykkyk ppz Φ∂∂=∂∂≡
( ) ( ))(,)()()()( kkukukysignk pz Φ∂∂=
system response direction
( )
( ) ⎥⎦
⎤⎢⎣
⎡⎟⎠⎞
⎜⎝⎛ +⎟⎠⎞
⎜⎝⎛ −−⎟
⎠⎞
⎜⎝⎛ +⎟⎠⎞
⎜⎝⎛ −−=
∂∂≡Φ
au
auab
au
auea
au
uu
1121,11
21,
,
θ
pp
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[ ] [ ]TTba 011)0()0()0()0( == θp
Example :
I.C.
Setpoint : 5=dy
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Learning rate : 15.0=η
System response direction: ( ) 1)()( =∂∂ kukysign
22
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Simulation results
23
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SNC shape tuning progress
u
e
24
Direct adaptive control schemes for chemical processes using SNCs
A SNC-based control scheme for large time-delay processes(Chen, 2001)
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A SNC-based control scheme for non-minimum phase processes(Chen, 2001)
)()()( sGsGsG ppp+−=
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A decentralized SNC control scheme for multi-input/multi-output processes (Chen and Yen, 1998)
• Consider an multivariable process described by
• In loop , the produces its controller output through the following nonlinear mapping (Assume loop paring results are: )
nn×
⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢
⎣
⎡
⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢
⎣
⎡
=
⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢
⎣
⎡
)(
)()(
)()()(
)()()()()()(
)(
)()(
2
1
21
22221
11211
2
1
su
susu
sGsGsG
sGsGsGsGsGsG
sy
sysy
nnnnn
n
n
n
M
L
MOMM
L
L
M
i iSNC
( ) [ ][ ]{ }[ ][ ]ii
iiiii teb
tebatu
θθ
−−+−−−
=)(exp1)(exp1~
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ii uy ↔
27
A static decoupler for the decentralized SNC control system: the decoupling gain can be given simply by
Parameter tuning algorithm (in continuous form) for
where and
nieti
Ti
iiii ,,2,1,1
)( K& =+
=zz
zp η
( ) ( )iiiii uuysign pz ,~~ Φ∂∂≡
( )
( )T
i
i
i
iii
i
i
i
iiii
i
i
iiii
au
auba
au
auea
au
uu
⎥⎦
⎤⎢⎣
⎡⎟⎟⎠
⎞⎜⎜⎝
⎛+⎟⎟
⎠
⎞⎜⎜⎝
⎛−−⎟⎟
⎠
⎞⎜⎜⎝
⎛+⎟⎟
⎠
⎞⎜⎜⎝
⎛−−=
∂∂≡Φ
~1
~1
21,
~1
~1
21,
~
~,~
θ
pp
)( jiDij ≠
ii
ij
ii
ij
sij
KK
sGsG
D
−=
−=→ )(
)(lim
0
iSNC
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A decentralized SNC scheme for 2x2 processes
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Some alternative SNC controllers and their parameter tuning algorithms
A bounded SNC (Chen and Peng, 1999) For handling with the input constraint of ,a bounded nonlinear controller of the form
where
the parameter tuning algorithm for the bias parameter
maxmin )( utuu ≤≤
( ) ( )( ) ( )( )[ ]minmax~1~1
21 utuututu −++=
( ) ( )( )[ ]( )( )[ ]θ
θ−−+−−−
=tebteb
tuexp1exp1~
( ) ( ) ( )( ) ( )( ) ⎟⎟⎠
⎞⎜⎜⎝
⎛∂∂
+−−=uy
signtututebt ~1~1ηθ&
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A SNC for the temperature trajectory control of a batch process (Chen and Peng, 1998)
0)( =tu
( ) ( )( )[ ]θ−−+=
tebtu
exp11
)())(1()()( tetutubt −−= ηθ&
• To achieve tight temperature tracking controlBoth heating and cooling of the processunit are necessary
A parametric variable is used to express the twomanipulated variables simultaneously
1)( =tu
maximum cooling and minimum heating
The simplified SNC
:
: maximum heating and minimum cooling
• Parameter tuning algorithm
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Unsolved Problem ?
Fact:System performance depends on the initial SNC controller parameters.
Question: How to start up SNC systematically?
Model-based SNC control systems
32
θ *e
*e−
du
The typical function
characteristics of the SNC
Model-based design of SNC control systemsSNC control of first-order processes
─ upper/lower limit part
─ linear part
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Analysis of the SNC closed-loop control system
Case 1: upper/lower part
Closed-loop dynamics ⎩⎨⎧
−<<−>>
= *
*
,,
)(eea
eeatu
⎪⎩
⎪⎨⎧
−<<−>>
=+ *
*
,,
eeaKeeaK
yyp
p&τ
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θ *e
*e−
du
34
Case 2: linear partsince
,0=e )exp(1)]exp(1[
θθ
bbauu d +
−==
,θ=e 0=u
⎟⎠⎞
⎜⎝⎛ −=
θ)(1)( teutu d
⎟⎟⎠
⎞⎜⎜⎝
⎛+−
=)exp(1))exp(1θθ
bbaud
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θ *e
*e−
du
Approximated linear function
35
The closed-loop system dynamics in this case can be represented by
Let , we arrive at
or
where is the time constant of the closed-loop system and is an index regarding the system performance
The value of can be given by
⎟⎠⎞
⎜⎝⎛ −−=+
θτ yyuKyy d
dp 1&
ddP yuK =
ddd yuKyuKy ⎟⎠⎞
⎜⎝⎛ −=⎟
⎠⎞
⎜⎝⎛ −+
θθτ 11&
dyyy =+&'τ
ταθττ ≡−= )/1/(' dy)/( dy−= θθα
θ
dy1−
=ααθ
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Also, from we have
we obtain from the solution of that
Let , then the above equation leads to
ddP yuK = 0)exp(1)exp(1>
−+
=θθ
bb
Kya
p
d
⎪⎩
⎪⎨⎧
−<<−>>
=+ *
*
,,
eeaKeeaK
yyp
p&τ
⎟⎠⎞⎜
⎝⎛ −
−+
=
−=
−
−
τ
τ
θθ t
P
d
t
P
ebb
Ky
eaK
ty
1)exp(1)exp(1
)1()(
⎟⎟⎠
⎞⎜⎜⎝
⎛−
=P
d
Kysign
eb
121ln1
4αθ
dt yty == '4)( τ
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The SNC parameter value setting procedure is summarized as follows:
• Given a performance factor , , and the desired process’s output value
α 10 <<αdy
one can calculate sequentially the values of , and from θ b a
)exp(1)exp(1
121ln1
1
4
θθ
θ
ααθ
α
bb
Kya
Kysign
eb
y
p
d
P
d
d
−+
=
⎟⎟⎠
⎞⎜⎜⎝
⎛−
=
−=
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Hard input constraintset
Thus from
we have and then
Together with and under the condition of ,
we obtain
uu ≤
ua =0
)exp(1)exp(1>
−+
=θθ
bb
Kya
p
d
)exp(1)exp(1
θθ
bb
Kyua
P
d
−+
==
dt yty =′= τ4)(
⎟⎟⎟⎟
⎠
⎞
⎜⎜⎜⎜
⎝
⎛
−
+−
=
P
d
P
d
Kyu
Kyu
b ln1θ
⎟⎠⎞⎜
⎝⎛ −
−+
=−τ
θθ t
P
d
P
ebb
Ky
Kty 1
)exp(1)exp(1)(
dP
d yuK
y1
and1ln41
−=⎟
⎟⎠
⎞⎜⎜⎝
⎛−−=
ααθα
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Table 1a. SNC parameter settings for
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0≠dy
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Table 1b. SNC parameter settings for the case of 0=dy
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0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50
0.2
0.4
0.6
0.8
1
syst
em o
utpu
t
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
1
1.5
2
2.5
3
time
cont
rol i
nput
α=0.3
α=0.5α=0.7
Example 1: First-order system Assume CASE 1: Effects of on system performance ( , )
( )1+
=sK
sG pP τ
1=pK 1=τ1=dy
α
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0 10 20 30 400
0.2
0.4
0.6
0.8
1
syst
em o
utpu
t
0 10 20 30 400.8
1
1.2
1.4
1.6
1.8
time
cont
rol i
nput
τp=1
τp=5
τp=10
different time constants ( fixed)1=pK
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)/'(5.0 ττα =CASE 2:
different process gain ( fixed)
0 2 4 6 8 100
0.2
0.4
0.6
0.8
1
syst
em o
utpu
t
0 2 4 6 8 100
0.5
1
1.5
2
time
cont
rol i
nput
kp=1
kp=5
kp=10
1=τ
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If the hard input constraint is
one can calculate the performance
index as
for the case of and
SNC controller parameters
2≤u
1733.0=α
1=PK 1=dy
2=a2412.5=b
2096.0−=θ
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CASE 3: Hard input constraint
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Model-based SNC control of a first-order plus dead-time processes
• First-order plus dead-time (FOPDT) process with transfer function of
where
The feedforward compensator
is designed as
)exp()()( stsGsG dp −=
)1()( += sKsG p
τ
)()(
)(sGsG
sGp
dff −=
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Example 2 Process: ,
The feedforward controller :
Setpoint:
Let , the SNC controller parameter vector is set as
The IMC-PID controller is given by (Brosilow and Joseph, 2001 )
( ) sP e
ssG −
+=
11 ( ) s
d es
sG 5.0
141 −
+=
141)(++
−=sssG ff
1=dy5.0=α
[ ] [ ]TTbap 16231.21565.1 −== θ
=)(sGPID ( )]1090.0179.024.111[610.0 +++ sss
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0 10 20 30 40 50 60 700
0.5
1
1.5
syst
em o
utpu
t
0 10 20 30 40 50 60 700.5
1
1.5
2
time
cont
rol i
nput
SNCIMC-PID
The performance comparison of SNC with the IMC-PID controller
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A direct adaptive model-based SNC control system• The presence of process uncertainties and nonlinearities
plant/model mismatch
In this situation, the associated SNC parameter tuning algorithm should be implemented to update the parameters.
direct adaptive SNC control system
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Example 3: SNC control of a nonlinear process
A bioreactor
XXDX μ+−=&
( ) XY
SSDSSX
f μ1−−=&
( )XPDP βμγ ++−=&
im
mm
KSSK
SPP
2
1
++
⎟⎟⎠
⎞⎜⎜⎝
⎛−
=μ
μ
μ is the specific growth rate
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From open loop test, we have the process model
and the disturbance model
The feedforward controller
The control objective is to regulate the concentration of cell mass at its desired value by manipulating the dilution rate
( ) sP e
ssG −
+−
=14.2
576.20
( ) sd e
ssG −
+=
1325.51092.0
( )576.2056.109
1092.0262.0++
=s
ssGff
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Based on the identified model and let
We have the initial controller parameter as
Learning rate:
The PI controller set as and
(Henson and Seborg, 1991 )
,1.0=α
hgLKc ⋅−= 07.0 hI 5.4=τ
1.0=η
[ ] [ ]TTba 111.01644.61474.0)0()0()0()0( −−== θp
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0 50 100 150 200 250 300 350 400 4506
6.5
7
7.5
8
X
0 50 100 150 200 250 300 350 400 4500
0.05
0.1
0.15
0.2
0.25
time (hr)
D
SNCPI
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Substrate concentration: +25% variation (150 hr)-25% variation (300 hr)
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The parameter tuning progress
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─ Model : ( ) ( )∑=
−+=+N
iim ikuhky
111
─ Predictive model : ( ) ( ) ( ) ( )[ ]kykykyky mm −++=+ 11ˆ
( ) ( ) ( ) ( )kuhkqkyky Δ++=+ 11ˆ
where ( ) ( )∑=
−+= ΔN
ii ikuhkq
21
Since
( ) ( ) ( )kekku eΔΔ += φpφpΔ and ( ) ( ) ( )kykrke −=
Model-based SNC predictive control system
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Impulse response model
( ) ( ) [ ]{ }[ ])(exp1
)(exp1,θθ
−−+−−−
==ebebaeNLtu p
54
Then
( ) ( ) ( ) ( ) ( )[ 11
11ˆ 111
+++++
=+ Δ krhkhkqkrh
ky ee
φφ
pφp
( ) ( )]keh eφ11+−Objective function
( ) ( )[ ] ( ) ( )kkkykrwJ T pWp ΔΔ++−+= 22
1 211ˆ1
21
( ) 0=∂∂Δ k
Jp ( )
( ) ee
T
hhw
hhw
kφφ 1
11
1
221
211
11 +⎥⎥⎦
⎤
⎢⎢⎣
⎡+
+=
−
Δppp φ
Wφφ
p
( ) ( ) ( )⎥⎦
⎤⎢⎣
⎡+
+−−+
+kekq
hkrkr
h ee φφ 11 11)()1(
11
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“One-step ahead MPC learning algorithm”
55
Model-based SNC predictive control of large time-delay processes
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• Actual process( ) s
p es
sG 9
15.11 −
+−
=
• Process model
( ) ( ) stm
desGsG −=
and ( ) sd e
ssG 30
155.0 −
+=
( )12
25.1+
−=
ssG 10=dtwhere ,
[ ] [ ]TTba 08318.09618.0)0()0()0()0( −== θp
• CASE 1: Disturbance rejection d(s)=1/s
Simulation studies (large time delay + plant/model mismatch)
• CASE 2: Setpoint change
5.0=α,
[ ] [ ]TTba 18318.00332.2)0()0()0()0( −−== θp 5.0=α,
1−=dyto
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1=dy
Sampling time = 0.5
57
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Disturbance rejection
58
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Setpoint tracking
60
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Direct Nonlinear Control Using SNC
Consider the SNC control of integrating process of order n
)exp(1)]exp(1[)(
φφ
bbay n
−+−−
= θφ −−= yyd,
and let ( generator )
∑−
=
−− =+++=
1
1
)()1(1
''2
'1
n
i
ii
nn yyyy λλλλθ L
where
dyy
ba,θ
: setpoint
: process output
: controller parameters
: designed variable
φ
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θ
62
• Case 1
When is largeφ
ay n ±=)(
⎩⎨⎧
<>
00
φφ
natn
y!
1±=
0)0( =y, if
φ
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• Case 2
When is smallφ
φ2
)( aby n ≅ ( )θ−−= yyabd2
⎟⎠
⎞⎜⎝
⎛−−= ∑
−
=
1
1
)(
2
n
i
iid yyyab λ
φTaking Laplace transformation
121
)()(
11
1 ++++=
−− sss
absYsY
nn
nd λλ L
( )ns 11+
=ε n
ab2
=ε,
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• Implementation
)exp(1)]exp(1[
φφ
bba
−+−−
ns1
θgenerator
dy φ
y
)(ny y
Nonlinear controller called NLC
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SNC control of integrating process of order n
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⎥⎦
⎤⎢⎣
⎡⎟⎠
⎞⎜⎝
⎛−−−
⎥⎥⎦
⎤
⎢⎢⎣
⎡⎟⎟⎠
⎞⎜⎜⎝
⎛⎟⎠
⎞⎜⎝
⎛−−−
=
∑
∑
=
=
2
0
)(
2
0
)(
'''
exp1
exp1
i
iid
i
iid
yyb
yybay
λ
λ
( )⎪⎪
⎩
⎪⎪
⎨
⎧
+=
+++=
±=
3
012
23
2
11
21
)()(
!31
ssssab
sYsY
aty
d ελλλ
''2
'1 yyyyd λλφ −−−=
Example: SNC control of integrating process of order 3
,
ab
23 =ε 22 3ελ = ελ 31 =
large & positive
,,
FCU PSE Lab., C.T. Chen
66
• SNC control of integrating process3
1s
Stepoint change has been made at t=1
0 1 2 3 4 5 6 7 8 9 100
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1a=10, b=1 ( ε = 0.5848)a=100, b=1 ( ε = 0.2714)a=1000, b=1 ( ε = 0.1260)
FCU PSE Lab., C.T. Chen
67
01
01)(asasabsbsbsG n
n
mm
p ++++++
=L
Lmn ≥,
• Implementation to general linear processes
01
011bsbsbasasa
s mm
nn
mn ++++++
− L
L
01
01
++++++
sasabsbsb
nn
mm
L
Ldy ye )( mny − u
( ) mnd ssY
sY−+
=11
)()(
ε
Controller
FCU PSE Lab., C.T. Chen
68
245035106)( 234 ++++
+=
ssssssG
Example: no modeling error
0 1 2 3 4 5 6 7 8 9 100
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Time
a=10, b=1 ( ε = 0.5848)a=10, b=2 ( ε = 0.4642)
FCU PSE Lab., C.T. Chen
690 5 10 15
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Tim e
a= 10, b= 1 ( ε = 0.5848)a= 10, b= 1 ( ε = 0.5848)
Example: modeling error (plant/model mismatch)
0625.59935.511212)( 234 ++++
+=
ssssssGp
modeling error
no modeling error
245035106)( 234 ++++
+=
ssssssGActual process :
Process model :
FCU PSE Lab., C.T. Chen
70
• Application to Nonlinear Process Control
( ) ( )( )⎩
⎨⎧
=+=
xhyuxgxfx&
[ ]Trnrfff hLhLhLhT −−= ηηη LML 2112
System :
Let
1
32
21
),()()(
ξηξη
ξ
ξξ
ξξ
==
+=
=
=
yq
uxaxbr
&
&
M
&
&
⎪⎪⎩
⎪⎪⎨
⎧
=
=
=
+
−
irfi
rf
rfg
TLq
hLb
hLLa 1
rni −= ,,2,1 L
,
,
relative degree = r
FCU PSE Lab., C.T. Chen
71
Let ( )[ ]( )φ
φbbavuxaxb
−+−−
==+exp1exp1)()(
)(
)(),(
),(1 hhLL
xhLva
bvu rfg
rf−
−=
−=
ηξηξ
( )[ ]( )φ
φξbbay r
r −+−−
==exp1exp1)(&
( )rd ssYsY
11
)()(
+=
ε
( ) 111
++ rsε
Better than input-output linearization technique (A. Henson and E. Seborg, Nonlinear process control, 1997) by one order
i.e. ,
i.e. ,
FCU PSE Lab., C.T. Chen
72
Example: Nonlinear Bioreactor
( )
[ ]Xy
XDPP
Xy
SsDS
XDXX
sxf
=++−=
−−=
+−=
βγμ
μ
μ
&
&
&
1
im
mm
KSSK
SPP
2
01
++
⎟⎟⎠
⎞⎜⎜⎝
⎛−
=μ
μ
System :
where
FCU PSE Lab., C.T. Chen
73
[ ]TPSXx = Du = sxyy =~, ,
ux
xsx
x
xy
x
xxx
f
⎥⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡
−−
−+
⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢
⎣
⎡
+
−=⎥⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡
3
2
1
1
1
1
3
2
1
][~1
βγμ
μ
μ
&
&
&
1xhy ==
( )( )( )φ
φbbav
−+−−
=exp1exp1
Soθφ −−= yyd
yyd −=
( since , )1=r 0=θ
1
1
xxv
hLhLv
ug
f
−−
=−
=μ
11
)()(
+=
ssYsY
d ε ab2
=ε,
,
FCU PSE Lab., C.T. Chen
74
FCU PSE Lab., C.T. Chen
0 1 2 3 4 5 6 7 8 9 104.5
5
5.5
6
6.5closed-loop response for setpoint change
time (hr)
x1 (b
iom
ass
conc
.)
0 1 2 3 4 5 6 7 8 9 100
0.2
0.4
0.6
0.8
1
1.2
time (hr)
dilu
tion
rate
the proposedIONIMC
75
FCU PSE Lab., C.T. Chen
0 10 20 30 40 50 60 70 805.9
5.95
6
6.05Closed-loop response for -20% Y disturbance
time (hr)
x1 (b
iom
ass
conc
.)
0 10 20 30 40 50 60 70 800.12
0.14
0.16
0.18
0.2
0.22
time (hr)
dilu
tion
rate
the proposedIONIMC
76
0 5 10 15 20 25 30 35 40 45 505.8
5.9
6
Closed-loop response in the presence of measurement noise
x1 (b
iom
ass
conc
.)
0 5 10 15 20 25 30 35 40 45 500.05
0.15
0.25
0.35
dilu
tion
rate
0 5 10 15 20 25 30 35 40 45 50
-0.01
0
0.01
time (hr)
nois
e si
gnal
FCU PSE Lab., C.T. Chen
77
ConclusionsWe have surveyed the recent direct adaptive control strategies developed based on using the SNCs.
Some alternative SNC-based control schemes as well as the associated convergence properties have been addressed for the purpose of dealing with diversified process dynamics.
New results on how to start up the SNC systematically have
been presented.
─ No input constraint: the SNC parameter values can be given by
simply assigning a performance index.
─ on the other hand, a SNC parameter settling formula is provided for the
case that there is a hard input constraint involved.
FCU PSE Lab., C.T. Chen
78
Extensive simulation results reveal that, with the systematic parameter setting formula, the pre-specified performance of the SNC control system can be ensured if the model is perfect.
Under the situation of plant/model mismatch, the SNC parameter
tuning algorithm can provide a more satisfactory control performance
as compared with conventional linear controllers.
Alternative model-based SNC control systems are also developed.
--- one-step ahead predictive SNC control
--- nonlinear SNC direct control
FCU PSE Lab., C.T. Chen
79
Based on its simple structure and effective algorithms, the proposed SNC-based control systems present to be a promising approaches to the direct adaptive control of chemical processes.
FCU PSE Lab., C.T. Chen
80
Thanks for your attention.
FCU PSE Lab., C.T. Chen
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