Automating Google Workspace (GWS) & more with Apps Script
Designs of Single Neuron Control Systems: Survey ~陳奇中教授演講投影片
1. Direct Adaptive Process Control Based on
Using a Single Neuron Controller:
Survey and Some New Results
陳奇中
Chyi-Tsong Chen
ctchen@fcu.edu.tw
Department of Chemical Engineering
Feng Chia University
Taichung 407, Taiwan
FCU PSE Lab., C.T. Chen
逢甲大學化工系 1
2. Outline
Introduction
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
2
FCU PSE Lab., C.T. Chen
3. Introduction
Conventional control strategies and limitations
Structure and design methodologies
─ Open-loop control
─ Manual control
─ Suitable for process whose mathematical model is hard to
characterize precisely 3
FCU PSE Lab., C.T. Chen
4. Closed-loop control system
─ Use system output error to generate control signal
─ Automatic control
─ Widely used algorithm: PID type controller
4
FCU PSE Lab., C.T. Chen
5. PID controller for continuous system
⎡ 1 de ( t ) ⎤
∫0 e ( t ) dt + τ
t
u (t ) = k c ⎢e (t ) +
τI dt ⎥
D
⎣ ⎦
PID controller for discrete system
⎡ Ts k τD ⎤
u (k ) = kc ⎢e(k ) + ∑ e (i ) + ( e ( k ) − e ( k − 1 )) ⎥
⎣ τI i=0 Ts ⎦
k c : proportional gain
τ I : integral time constant
τ D : derivative time constant
TS : sampling time
5
FCU PSE Lab., C.T. Chen
6. 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
Motivation:
Searching for new approaches for complex process control
Artificial Intelligence (AI)
6
FCU PSE Lab., C.T. Chen
8. Introduction to artificial neural networks
Structure of neurons
An artificial neuron
8
FCU PSE Lab., C.T. Chen
9. Multilayer feedforward neural network
receive signals transmit output
from external Signal signals to
environment transmission environment 9
FCU PSE Lab., C.T. Chen
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
10
FCU PSE Lab., C.T. Chen
11. Properties (advantages) of MNN
1. 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.
11
FCU PSE Lab., C.T. Chen
12. Direct adaptive control using a shape-tunable neural
network controller (Chen and Chang, 1996)
What happen when some neurons of the neural network were
broken down?
+
single neuron controller
12
FCU PSE Lab., C.T. Chen
13. The single neuron controller (SNC)
and its parameter tuning algorithm
Single neuron controller
a { 1 − exp [− b(e − θ ) ]}
u (t ) = NL( e, p ) =
1 + exp [ − b (e − θ )]
e(t ) process output error, given by e(t ) = yd (t ) − y (t )
p controller parameter vector, defined as p ≡ [a, b, θ ]T
a control output level
b slope (sensitivity factor)
θ bias
e u
e −θ
13
FCU PSE Lab., C.T. Chen
17. A SNC-based direct adaptive control scheme
+ e ek uk u
yd y
17
FCU PSE Lab., C.T. Chen
18. SNC parameters tuning algorithm
1
─ System performance E (k ) = ( yd − y (k )) 2
2
─ Parameter tuning algorithm (Chen, 2001)
z (k )
p(k + 1) = p(k ) + η e(k )
1 + z ( k )T z ( k )
where
z ( k ) ≡ ∂y ( k ) ∂ p ( k ) = ( ∂y ( k ) ∂u ( k ) ) Φ ( u ( k ) , p ( k ) )
and
Φ ( u ,p ) ≡ ∂ u ∂ p
⎡u 1 ⎛ u ⎞⎛ u⎞ 1 ⎛ u ⎞⎛ u⎞⎤
= ⎢ , a ( e − θ )⎜ 1 − ⎟ ⎜ 1 + ⎟ , − ab ⎜ 1 − ⎟ ⎜ 1 + ⎟ ⎥
⎣a 2 ⎝ a ⎠⎝ a⎠ 2 ⎝ a ⎠⎝ a⎠⎦
18
FCU PSE Lab., C.T. Chen
19. Stability of the SNC parameter learning algorithm
Assume z(k ) is bounded
Let 0 < η < 2 ;
the controller parameter vector p converges to its local
optimal p * asymptotically, where NL (0 , p ∗ ) = u d (the
desired control input) and e(p*) = 0 .
For the theoretical and rigorous proof, please refer to Chen
(2001).
19
FCU PSE Lab., C.T. Chen
20. A simplified version of the learning algorithm
--- Using system response direction
parameter tuning algorithm (Chen, 2001)
z (k )
p(k + 1) = p(k ) + η e(k )
1 + z ( k )T z ( k )
where
z ( k ) ≡ ∂y ( k ) ∂ p ( k ) = ( ∂y ( k ) ∂u ( k ) )Φ ( u ( k ) , p ( k ) )
system response direction
z ( k ) = sign ( ∂y ( k ) ∂u ( k ) ) Φ ( u ( k ) , p ( k ) )
Φ ( u ,p ) ≡ ∂ u ∂ p
⎡u 1 ⎛ u ⎞⎛ u ⎞ 1 ⎛ u ⎞⎛ u⎞⎤
= ⎢ , a ( e − θ ) ⎜ 1 − ⎟ ⎜ 1 + ⎟ , − ab ⎜ 1 − ⎟ ⎜ 1 + ⎟ ⎥
⎣a 2 ⎝ a ⎠⎝ a⎠ 2 ⎝ a ⎠⎝ a⎠⎦
20
FCU PSE Lab., C.T. Chen
21. Example :
Setpoint : yd = 5
p(0) = [ a(0) b(0) θ (0) ] = [1 1 0]
T T
I.C.
Learning rate : η = 0.15
System response direction: sign ( ∂y ( k ) ∂u ( k ) ) = 1 21
FCU PSE Lab., C.T. Chen
23. u
SNC shape
tuning progress
e
23
FCU PSE Lab., C.T. Chen
24. Direct adaptive control schemes for
chemical processes using SNCs
A SNC-based control scheme for large time-delay processes
(Chen, 2001)
24
FCU PSE Lab., C.T. Chen
25. A SNC-based control scheme for non-minimum phase processes
(Chen, 2001)
− +
G p ( s) = G p ( s)G p ( s)
25
FCU PSE Lab., C.T. Chen
26. A decentralized SNC control scheme for multi-input/multi-
output processes (Chen and Yen, 1998)
• Consider an n × n multivariable process described by
⎡ y1 ( s ) ⎤ ⎡G11 ( s ) G12 ( s ) L G1n ( s ) ⎤ ⎡ u1 ( s) ⎤
⎢ y ( s ) ⎥ ⎢G ( s ) G ( s ) L G2 n ( s )⎥ ⎢u2 ( s) ⎥
⎢ 2 ⎥ = ⎢ 21 22 ⎥⎢ ⎥
⎢ M ⎥ ⎢ M M O M ⎥⎢ M ⎥
⎢ ⎥ ⎢ ⎥⎢ ⎥
⎣ yn ( s )⎦ ⎣Gn1 ( s ) Gn 2 ( s) L Gnn ( s ) ⎦ ⎣un ( s)⎦
• In loop i , the SNC i produces its controller output through the following
nonlinear mapping (Assume loop paring results are: y i ↔ u i )
~ (t ) = a i { 1 − exp [ − bi [ ei (t ) − θ i ] ] }
ui
1 + exp [ − b [ ei (t ) − θ i ] ]
26
FCU PSE Lab., C.T. Chen
27. Parameter tuning algorithm (in continuous form) for SNC i
zi
p i (t ) = η i ei
& , i = 1, 2, K , n
1 + zi zi
T
where z i ≡ sign ( ∂ yi ∂ ui ) Φ ( ui , p i ) and
~ ~
Φ ( ui , p i
~ ) ~
≡ ∂ ui ∂p i
~
⎡ ui 1 ⎛ ~
ui ⎞ ⎛ ~ ⎞ 1 ⎛ ~
ui ⎞ ⎛ ~ T
ui ⎞ ⎤
= ⎢ , ai ( ei − θ i )⎜ 1 − ⎟ ⎜ 1 + ui
⎜ ai ⎟ ⎜
⎟
⎟ , − 2 aibi ⎜ 1 − a ⎟ ⎜ 1 + a ⎟ ⎥
⎜ ⎟⎜ ⎟
⎣ ai 2 ⎝ ⎠⎝ ai ⎠ ⎝ i ⎠⎝ i ⎠⎦
A static decoupler for the decentralized SNC control system:
the decoupling gain Dij (i ≠ j ) can be given simply by
Gij ( s )
Dij = − lim
s →0 Gii ( s)
K ij
=−
K ii 27
FCU PSE Lab., C.T. Chen
29. Some alternative SNC controllers and their
parameter tuning algorithms
A bounded SNC (Chen and Peng, 1999)
For handling with the input constraint of u min ≤ u (t ) ≤ u max ,
a bounded nonlinear controller of the form
1
u (t ) = [ ( 1 + u (t ) ) u max + ( 1 − u (t ) ) u min
~ ~ ]
2
where
~ (t ) = 1 − exp [ − b
u
( e (t ) − θ ) ]
1 + exp [ − b ( e (t ) − θ ) ]
the parameter tuning algorithm for the bias parameter
θ& (t ) = −η b e (t ) ( 1 − u (t ) ) ( 1 + u (t ) ) sign ⎛ ∂ y ⎞
~ ~ ⎜ ⎟
⎜ ∂u ⎟
⎝ ⎠ 29
FCU PSE Lab., C.T. Chen
30. A SNC for the temperature trajectory control of a batch
process (Chen and Peng, 1998)
• To achieve tight temperature tracking control
Both heating and cooling of the process
unit are necessary
A parametric variable is used to express the two
manipulated variables simultaneously
u (t ) = 0 : maximum cooling and minimum heating
u (t ) = 1 : maximum heating and minimum cooling
The simplified SNC
1
u (t ) =
1 + exp [ − b(e(t ) − θ )]
• Parameter tuning algorithm
&
θ (t ) = −η b u (t ) (1 − u (t )) e(t ) 30
FCU PSE Lab., C.T. Chen
31. Unsolved Problem ?
Fact:
System performance depends on the initial
SNC controller parameters.
Question:
How to start up SNC systematically?
Model-based SNC control systems
31
32. Model-based design of SNC control systems
SNC control of first-order processes
The typical function
characteristics of the SNC
− e*
θ e*
─ upper/lower limit part ud
─ linear part
32
FCU PSE Lab., C.T. Chen
33. Analysis of the SNC closed-loop control system
Case 1: upper/lower part
⎧ a, e >> e*
u (t ) = ⎨
⎩ − a, e << −e*
− e*
Closed-loop dynamics θ e*
ud
⎧ K p a, e >> e*
⎪
τ y + y = ⎨
&
⎪− K p a, e << −e
*
⎩
33
FCU PSE Lab., C.T. Chen
34. Case 2: linear part
since
a[1 − exp( b θ )]
e = 0, u = ud =
1 + exp( b θ )
e =θ, u = 0
Approximated linear function − e*
θ e*
⎛ e(t ) ⎞
u (t ) = ud ⎜ 1 − ⎟ ud
⎝ θ ⎠
⎛ 1 − exp(b θ )) ⎞
u d = a⎜
⎜ 1 + exp(b θ ) ⎟⎟
⎝ ⎠
34
FCU PSE Lab., C.T. Chen
35. The closed-loop system dynamics in this case can be represented by
⎛ y − y⎞
τ y
& + y = K p u d ⎜1 − d ⎟
⎝ θ ⎠
Let K P ud = yd , we arrive at
⎛ K ud ⎞ ⎛ K ud ⎞
τ y + ⎜1 −
& ⎟ y = ⎜1 − ⎟ yd
⎝ θ ⎠ ⎝ θ ⎠
or
τ ' y + y = yd
&
where τ ' = τ /(1 − yd / θ ) ≡ α τ is the time constant of the closed-loop system
and α = θ /(θ − yd ) is an index regarding the system performance
The value of θ can be given by
α
θ = yd
α − 1 35
FCU PSE Lab., C.T. Chen
36. Also, from K P ud = yd we have
yd 1 + exp(bθ )
a = >0
K p 1 − exp(bθ )
⎧ K p a, e >> e*
⎪
we obtain from the solution of τ y + y = ⎨
& * that
⎪− K p a, e << −e
⎩
y (t ) −t
= a (1 − e τ )
KP
yd 1 + exp(bθ ) ⎛
⎜1 − e τ ⎞
−t
= ⎟
K P 1 − exp(bθ ) ⎝ ⎠
Let y (t ) t =4τ ' = yd , then the above equation leads to
1 1 ⎛ yd ⎞
b = ln sign⎜
⎜K ⎟ ⎟
θ 2 e 4α −1 ⎝ P⎠
36
FCU PSE Lab., C.T. Chen
37. The SNC parameter value setting procedure is summarized
as follows:
• Given a performance factor α , 0 < α < 1, and the desired process’s output
value y d
one can calculate sequentially the values of θ , b and a from
α
θ = yd
α − 1
1 1 ⎛ yd ⎞
b = ln 4α
sign ⎜
⎜K ⎟
⎟
θ 2e − 1 ⎝ P ⎠
y d 1 + exp( b θ )
a =
K p 1 − exp( b θ )
37
FCU PSE Lab., C.T. Chen
38. Hard input constraint u ≤ u
set
a=u
yd 1 + exp(bθ )
Thus from a =
K p 1 − exp(bθ )
>0
⎛ y ⎞
⎜ u+ d ⎟
y d 1 + exp(bθ ) −1 ⎜ KP ⎟
we have a=u = and then b= ln
θ ⎜ u − yd ⎟
K P 1 − exp(bθ )
⎜ KP ⎟
⎝ ⎠
y(t ) yd 1 + exp(bθ ) ⎛
⎜1 − e τ ⎞
−t
Together with = ⎟ and under the
KP KP 1 − exp(bθ ) ⎝ ⎠
condition of y (t ) t =4τ ′ = yd ,
we obtain 1 ⎛⎜1 −
yd ⎞
⎟ and θ = α yd
α = − ln⎜
4 ⎝ KP u ⎟
⎠ α −1
38
FCU PSE Lab., C.T. Chen
39. Table 1a. SNC parameter settings for yd ≠ 0
39
FCU PSE Lab., C.T. Chen
40. Table 1b. SNC parameter settings for the case of yd = 0
40
FCU PSE Lab., C.T. Chen
41. Kp
Example 1: First-order system GP (s ) =
τs + 1
Assume yd = 1
CASE 1: Effects of α on system performance ( Kp =1 , τ =1 )
1
α =0.3
system output
0.8 α =0.5
0.6 α =0.7
0.4
0.2
0
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
3
control input
2.5
2
1.5
1
41
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
FCU PSE Lab., C.T. Chen time
42. CASE 2: α = 0.5 (τ ' / τ )
different time constants different process gain
( K p = 1 fixed) ( τ = 1 fixed)
1 1
system output
system output
k p=1
0.8 τp=1 0.8
k p=5
0.6 τp=5 0.6
k p=10
0.4 τp=10 0.4
0.2 0.2
0 0
0 10 20 30 40 0 2 4 6 8 10
1.8 2
control input
control input
1.6
1.5
1.4
1
1.2
0.5
1
0.8 0
0 10 20 30 40 0 2 4 6 8 10
time time 42
FCU PSE Lab., C.T. Chen
43. CASE 3: Hard input constraint
If the hard input constraint is u ≤ 2
one can calculate the performance
index as α = 0.1733
for the case of K P = 1 and y d = 1
SNC controller parameters
a=2
b = 5.2412
θ = −0.2096
43
FCU PSE Lab., C.T. Chen
44. Model-based SNC control of a first-order plus dead-time
processes
• First-order plus dead-time (FOPDT) process with transfer function of
G p ( s ) = G ( s ) exp( −td s )
where
Kp
G(s) =
(τ s + 1)
The feedforward compensator
is designed as G ff ( s ) = − Gd ( s )
G p (s)
44
FCU PSE Lab., C.T. Chen
45. Example 2
1 1
Process: GP ( s ) = e −s , Gd ( s ) = e −0.5 s
s + 1 4s + 1
The feedforward controller : G ff ( s ) = − s + 1
4s + 1
Setpoint: y d = 1
Let α = 0.5, the SNC controller parameter vector is set as
p = [a b θ ]T = [ 1.1565 2.6231 − 1]
T
The IMC-PID controller is given by (Brosilow and Joseph, 2001 )
GPID (s) = 0.610[1 + 1 1.24 s + 0.179 s ( 0.090 s + 1 )]
45
FCU PSE Lab., C.T. Chen
46. The performance comparison of SNC with the IMC-PID controller
1.5
SNC
IMC-PID
system output 1
0.5
0
0 10 20 30 40 50 60 70
2
control input
1.5
1
0.5
0 10 20 30 40 50 60 70
time
46
FCU PSE Lab., C.T. Chen
47. 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
47
FCU PSE Lab., C.T. Chen
48. Example 3: SNC control of a nonlinear process
A bioreactor
X = −D X + μ X
&
1
S = D(S f − S ) −
& μX
YX S
P = − D P + (γ μ + β )X
&
μ is the specific growth rate
⎛ P ⎞
μm ⎜
⎜1 − ⎟S
⎝ Pm ⎟
⎠
μ =
S2
Km + S +
Ki
48
FCU PSE Lab., C.T. Chen
49. The control objective is to regulate the concentration of cell mass at
its desired value by manipulating the dilution rate
From open loop test, we have the process model
− 20.576 − s
GP ( s ) = e
2. 4 s + 1
and the disturbance model
0.1092
Gd ( s ) = e− s
5.325 s + 1
The feedforward controller
0.262s + 0.1092
Gff ( s ) =
109.56 s + 20.576
49
FCU PSE Lab., C.T. Chen
50. Based on the identified model and let α = 0.1,
We have the initial controller parameter as
p(0) = [ a(0) b(0) θ (0) ] = [0.1474 − 6.1644 − 0.111]
T T
Learning rate: η = 0.1
The PI controller set as K c = − 0.07 L g ⋅ h and τ I = 4.5 h
(Henson and Seborg, 1991 )
50
FCU PSE Lab., C.T. Chen
53. Model-based SNC predictive control system
N
─ Model : ym (k + 1) = ∑ hi u (k + 1 − i ) Impulse response model
i =1
─ Predictive model : y (k + 1) = ym (k + 1) + [ y (k ) − ym (k )]
ˆ
y (k + 1) = y (k ) + q(k ) + h1Δu (k )
ˆ
N
where q(k ) = ∑ hi Δu (k + 1 − i )
i =2
a { 1 − exp [− b(e − θ ) ]}
Since u (t ) = NL( e, p ) =
1 + exp [ − b (e − θ )]
Δu (k ) = φ p Δp(k ) + φe Δe(k ) and e(k ) = r (k ) − y (k )
53
FCU PSE Lab., C.T. Chen
54. Then
y (k + 1) =
ˆ
1
1 + h1φe
[
r (k ) + q (k ) + h1φ p Δp(k ) + h1φe r (k + 1)
− (1+ h1φe )e(k )]
Objective function
1 1 T
J = w1 [r (k + 1) − y (k + 1)] + Δ p(k )W2 Δp(k )
2
ˆ
2 2
−1
∂J ⎡ w h φpφ
2 T
⎤ w1h1φ p
=0 Δp(k ) = ⎢
1 1 p
+ W2 ⎥
∂Δp(k )
⎣ (1 + h1φe ) ⎥ 1 + h1φe
2
⎢ ⎦
⎡ 1 1 ⎤
⎢ (r (k + 1) − r (k ) ) − q (k ) + e(k )⎥
⎣1 + h1φe 1 + h1φe ⎦
“One-step ahead MPC learning algorithm”
54
FCU PSE Lab., C.T. Chen
56. Simulation studies (large time delay + plant/model mismatch)
• Actual process
− 1 −9 s 0.5 −30 s
G p (s ) = e and Gd (s ) = e
1.5s + 1 5s + 1
• Process model
− 1.25
G (s ) =
Gm (s ) = G (s )e −td s where
2s + 1 ,
t d = 10
• CASE 1: Disturbance rejection d(s)=1/s
p(0) = [ a(0) b(0) θ (0) ] = [0.9618 − 0.8318 0] , α = 0 .5
T T
• CASE 2: Setpoint change yd = 1 to yd = −1
p(0) = [ a(0) b(0) θ (0) ] = [2.0332 − 0.8318 −1] , α = 0 .5
T T
Sampling time = 0.5
56
FCU PSE Lab., C.T. Chen
61. Direct Nonlinear Control Using SNC
Consider the SNC control of integrating process of order n
a[1 − exp(−bφ )]
y (n)
= , φ = yd − y − θ
1 + exp(−bφ )
and let ( θ generator )
n −1
θ = λ1 y + λ2 y + L + λn −1 y
' '' ( n −1)
= ∑ λi y (i )
i =1
where
yd : setpoint φ
y : process output
a, b : controller parameters
θ : designed variable
61
FCU PSE Lab., C.T. Chen
62. • Case 1
When φ is large
⎧φ > 0
y (n)
= ±a ⎨
⎩φ < 0
φ
1 n
y = ± at
n!
, if y (0) = 0
62
FCU PSE Lab., C.T. Chen
63. • Case 2
When φ is small
ab
≅ φ = ( yd − y − θ )
(n) ab
y
2 2
ab ⎛ n −1
⎞
= ⎜ yd − y − ∑ λi y ( i ) ⎟
2 ⎝ i =1 ⎠ φ
Taking Laplace transformation
Y ( s) 1
=
Yd ( s ) 2 n
s + λn −1s n −1 + L + λ1s + 1
ab
1 2
=
(εs + 1)n , ε= n
ab 63
FCU PSE Lab., C.T. Chen
64. • Implementation
SNC control of integrating process of order n
Nonlinear controller
called NLC
yd φ a[1 − exp(−bφ )] y (n) 1 y
1 + exp(−bφ ) sn
y
θ
generator
64
FCU PSE Lab., C.T. Chen
65. Example: SNC control of integrating process of order 3
⎡ ⎛ ⎛ 2
(i ) ⎞ ⎞
⎤
a ⎢1 − exp⎜ − b⎜ yd − ∑ λi y ⎟ ⎟⎥
⎜ ⎟
⎢ ⎝ ⎝ ⎠ ⎠⎥
y '' ' = ⎣ ⎦
i =0
⎡ ⎛ 2
( i ) ⎞⎤
1 − exp ⎢− b⎜ yd − ∑ λi y ⎟⎥
⎣ ⎝ i =0 ⎠⎦
⎧ 1 2
⎪ y = ± at , φ = yd − y − λ1 y ' − λ2 y '' large & positive
3!
⎪
⎨ Y (s) 1 1
⎪Y ( s ) = 2 3 =
(εs + 1)3
⎪ d s + λ2 s + λ1s + λ0
2
⎩ ab
2
ε =
3
, λ2 = 3ε 2 , λ1 = 3ε
ab
65
FCU PSE Lab., C.T. Chen
66. 1
• SNC control of integrating process
s3
1
a=10, b=1 ( ε = 0.5848)
0.9 a=100, b=1 ( ε = 0.2714)
a=1000, b=1 ( ε = 0.1260)
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0 1 2 3 4 5 6 7 8 9 10
Stepoint change has been made at t=1 66
FCU PSE Lab., C.T. Chen
67. • Implementation to general linear processes
bm s m + L + b1s + b0
G p (s) =
a n s n + L + a1s + a0 , n≥m
e y ( n−m) 1 an s n + L + a1 s + a0 u bm s m + L + b1 s + b0
yd y
s n−m bm s m + L + b1s + b0 an s n + L + a1 s + 0
Controller
Y (s) 1
=
Yd ( s ) (εs + 1)n − m
67
FCU PSE Lab., C.T. Chen
69. Example: modeling error (plant/model mismatch)
s+6
Actual process : G (s) =
s 4 + 10 s 3 + 35s 2 + 50 s + 24
s + 12
Process model : G p ( s ) =
s 4 + 12 s 3 + 51.5s 2 + 93s + 59.0625
1
a= 10, b= 1 ( ε = 0.5848)
0.9
a= 10, b= 1 ( ε = 0.5848)
0.8
0.7 modeling error
0.6
0.5 no modeling error
0.4
0.3
0.2
0.1
0
0 5 10 15
T im e 69
FCU PSE Lab., C.T. Chen
70. • Application to Nonlinear Process Control
⎧ x = f ( x ) + g ( x )u
&
System : ⎨ relative degree = r
⎩ y = h( x )
Let [
T = h L f h L h L L h M η1 η 2 L η n − r
2
f
r −1
f ]
T
&
ξ1 = ξ 2
&
ξ 2 = ξ3
⎧a = Lg L f r −1h
M ⎪
, ⎪
b = Lf h
r
&
ξ r = b( x) + a ( x)u ⎨
⎪
η = q (ξ ,η )
& ⎪qi = L f Tr +i , i = 1,2,L, n − r
⎩
y = ξ1
70
FCU PSE Lab., C.T. Chen
71. a[1 − exp(− bφ )]
Let b( x) + a ( x)u = v =
1 + exp(− bφ )
v − L f h( x )
r
v − b(ξ ,η )
u= =
a (ξ ,η ) r −1
L g L f h( h)
i.e. ,
& = y ( r ) = a[1 − exp(− bφ )]
ξr
1 + exp(− bφ )
Y (s) 1
=
Yd ( s ) (εs + 1)r
Better than input-output linearization technique (A. Henson and E.
Seborg, Nonlinear process control, 1997) by one order
i.e. , 1
(εs + 1)r +1 71
FCU PSE Lab., C.T. Chen
72. Example: Nonlinear Bioreactor
System :
&
X = − DX + μX
& = D (s − S ) − 1 μX
S f
yx s
P = − DP + [γμ + β ]X
&
y=X
where
⎛ P0 ⎞
μ m ⎜1 − ⎟ S
⎜ P ⎟
μ= ⎝ m ⎠
S2
Km + S +
Ki
72
FCU PSE Lab., C.T. Chen
73. ~=y
x = [X P] , u=D ,
T
S y x s
⎡ x1 ⎤
& ⎡ μ x1 ⎤ ⎡ − x ⎤
⎢x ⎥ = ⎢ − ⎥ ⎢ 1
1
&
⎢ 2⎥ ⎢ ~ μ x1 ⎥ + ⎢ s f − x 2 ⎥ u
⎥
⎢ x3 ⎥ ⎢ y ⎥ ⎢ − x ⎥
⎣& ⎦ ⎢ [ γμ
⎣ + β ] x1 ⎥ ⎣
⎦ 3 ⎦
y = h = x1
a(1 − exp(− bφ )) φ = yd − y −θ
So v= = yd − y
1 + exp(− bφ ) ,
( since r =1, θ = 0 )
v − Lf h v − μx1
u= =
Lg h − x1
Y ( s) 1 2
=
Yd ( s ) εs + 1 , ε = ab
73
FCU PSE Lab., C.T. Chen
77. Conclusions
We 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. 77
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
78
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.
79
FCU PSE Lab., C.T. Chen