The document discusses Model Reference Adaptive Systems (MRAS) for adaptive control. It describes the MIT rule, which is the original approach for MRAS. The MIT rule adjusts controller parameters based on the error between system output and reference model output. Several examples are provided to illustrate parameter adaptation for systems with adjustable gain, first order, and second order dynamics using the MIT rule. The normalized MIT rule is also introduced to improve parameter convergence.
Objectives: This course will provide a comprehensive overview of power system stability and control problems. This includes the basic concepts, physical aspects of the phenomena, methods of analysis, the integration of MATLAB and SINULINK in the analysis of power system .
Course Content: 1. Power System Stability: Introduction
2. Stability Analysis: Swing Equation
3. Models for Stability Studies
4. Steady State Stability
5. Transient Stability
6. Multimachine Transient Stability
7. Power System Control: Introduction
8. Load Frequency Control
9. Automatic generation Control
10. Reactive Power Control
Objectives: This course will provide a comprehensive overview of power system stability and control problems. This includes the basic concepts, physical aspects of the phenomena, methods of analysis, the integration of MATLAB and SINULINK in the analysis of power system .
Course Content: 1. Power System Stability: Introduction
2. Stability Analysis: Swing Equation
3. Models for Stability Studies
4. Steady State Stability
5. Transient Stability
6. Multimachine Transient Stability
7. Power System Control: Introduction
8. Load Frequency Control
9. Automatic generation Control
10. Reactive Power Control
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Components in electric drives
DC drives classification in various types
Single-Phase Half-Wave-Converter Drives:
Three-phase drives
Ac motor drives
Introduction: AC Motor Drives
Stator Voltage Control:
Rotor Voltage Control:
Frequency Control:
DC DRIVES Vs AC DRIVES
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Load / Frequency balancing Control systems studyCAL
In this project, the load and frequency control problem on the power generator at 'Britannia sugar factory' is investigated under different governor action. The existing system employs a Mechanical-hydraulic governor. It is desired to improve the system's response to load disturbances on the interconnected power grid.
Permanent Magnet Synchronous motor (PMSM) or Permanent Magnet AC motor:
Introduction to PMSM motor.
Types of PMSM Motor.
Mathematical modelling of PMSM motor.
Advantages and dis Advantages of PMSM motor
Summary of Modern power system planning part one
"The Forecasting of Growth of Demand for Electrical Energy"
the main topic of this chapter is the analysis of the various techniques required for utility planning engineers to optimally plan the expansion of the electrical power system.
The sliding mode control approach is recognized as one of the
efficient tools to design robust controllers for complex high-order non-linear dynamic plant operating under uncertainty conditions.
It gives how states are representing in various canonical forms and how it it is different from transfer function approach. and finally test the system controllability and observability by kalman's test
Electrical AC & DC Drives in Control of Electrical DrivesHardik Ranipa
content
Introduction:
ELECTRIC DRIVES - A DEFINITION
Block diagram of Electrical drive:
Components in electric drives
DC drives classification in various types
Single-Phase Half-Wave-Converter Drives:
Three-phase drives
Ac motor drives
Introduction: AC Motor Drives
Stator Voltage Control:
Rotor Voltage Control:
Frequency Control:
DC DRIVES Vs AC DRIVES
Conclusion:
Thank u......
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2. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
1.1 Model Reference Adaptation Systems (MRAS)
MRAS is an important adaptive controller. It may be regarded as an adaptive servo system in which the desired performance is
expressed in terms of a reference model, which gives the desired response to a command signal. This is a convenient way to give
specifications for a servo problem. A block diagram of the system is shown in Figure 1.1. The system has an ordinary feedback
loop composed of the process and the controller in addition to another feedback loop that changes the controller parameters.
The parameters are changed on the basis of feedback from the error, which is the difference between the output of the system
and the output of the reference model. The ordinary feedback loop is called the inner loop, and the parameter adjustment loop
is called the outer loop. The mechanism for adjusting the parameters in a model-reference adaptive system can be obtained in
two ways: by using a gradient method or by applying stability theory.
Figure 1.1: Block diagram of a model-reference adaptive system
1.1.1 MIT Rule
The MIT rule is the original approach to model-reference adaptive control. The name is derived from the fact that it was
developed at the Instrumentation Laboratory (now the Draper Laboratory) at MIT.
To present the MIT rule, we will consider a closed-loop system in which the controller has one adjustable parameter θ.
The desired closed-loop response is specified by a model whose output is ym. Let e be the error between the output y of
the closed-loop system and the output ym of the model. One possibility is to adjust parameters in such a way that the loss
function J(θ) = 1
2e2 is minimized.
Procedure
Process : G(s) =
y
u
(1.1)
Model : Gm(s) =
ym
uc
(1.2)
Control law : u(t) = f(uc, y) (1.3)
Get closed loop from [1.1] & [1.3] :
y
uc
(1.4)
Error : e = y − ym (1.5)
∂e
∂θ
=
∂y
∂θ
(1.6)
MIT Rule :
dθ
dt
= −γe
∂e
∂θ
(1.7)
Mohamed Mohamed El-Sayed Atyya Page 2 of 40
3. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
Examples
1. Gain Adjustment
Gp(s) = θG(s) = θ
2
s2 + 2s + 4
Gm(s) = θoG(s) = θo
2
s2 + 2s + 4
; θo = 2
e = y − ym = θG(s)uc − θoG(s)uc
dθ
dt
= −γe
∂e
∂θ
= −γG(s)uce = −γ
ym
θo
e
Figure 1.2: Gain adjustment block diagram
At γ = 0.5
Mohamed Mohamed El-Sayed Atyya Page 3 of 40
4. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
At γ = 0.7
At γ = 1
Mohamed Mohamed El-Sayed Atyya Page 4 of 40
5. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
At γ = 1.2
At γ = 1.5
Mohamed Mohamed El-Sayed Atyya Page 5 of 40
6. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
2. First Order System Adjustment
dy
dt
= −ay + bu
G(s) =
Y
U
=
b
s + a
dym
dt
= −amy + bmu
G(s) =
Ym
U
=
bm
s + am
Use the control law : u(t) = touc(t) − soy(t)
U = toUc − soY = Y
s + a
b
⇒
Y
Uc
=
to
s+a
b + so
=
boto
s + a + bso
bm = bto
am = a + bso
e = Y − Ym =
bto
s + a + bso
Uc =
bm
s + am
Uc
∂e
∂to
=
b
s + a + bso
Uc =
b
s + am
Uc
∂e
∂so
=
−b2to
(s + a + bso)2
Uc =
−b
s + a + bso
Y =
−b
s + am
Y
dθ
dt
= −γ
∂e
∂θ
e
dto
dt
= −γ1
b
s + am
Uc e
dso
dt
= γ2
b
s + am
Y e
Let : a = 1, b = 2, am = 8, bm = 8
Figure 1.3: First order system adjustment block diagram
Mohamed Mohamed El-Sayed Atyya Page 6 of 40
7. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
At γ1 = 2, γ2 = 0.1
At γ1 = 2, γ2 = 1
Mohamed Mohamed El-Sayed Atyya Page 7 of 40
8. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
At γ10 = 2, γ2 = 0.5
At γ1 = 1, γ2 = 0.05
Mohamed Mohamed El-Sayed Atyya Page 8 of 40
9. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
3. Second Order System Adjustment
G(s) =
y
u
=
s + b
s2 + a1s + a0
Gm(s) =
ym
uc
=
s + bm
s2 + a1ms + a0m
Let the control law : u(t) = t0uc(t) − s0y(t)
y
s2 + a1s + a0
s + b
= t0uc − s0y
⇒
y
uc
=
t0s + bt0
s2 + (a1 + s0)s + a0 + bs0
e = y − ym
∂e
∂t0
=
s + b
s2 + (a1 + s0)s + a0 + bs0
uc ≈ Gm(s)uc
∂e
∂s0
=
−t0(s + b)(s + b)
[s2 + (a1 + s0)s + a0 + bs0]2
uc
=
−(s + b)
s2 + (a1 + s0)s + a0 + bs0
y =
−y2
t0uc
dθ
dt
= −γ
∂e
∂θ
e
dt0
dt
= −γ1Gmuce
ds0
dt
= γ2
y2
t0uc
e = γGmye
Figure 1.4: Second order system adjustment block diagram
Mohamed Mohamed El-Sayed Atyya Page 9 of 40
10. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
Let : G(s) = s+5
s2+4s+3 (stable), Gm(s) = s+10
s2+8s+10
At γ1 = γ2 = 10
At γ1 = 10, γ2 = 5
Mohamed Mohamed El-Sayed Atyya Page 10 of 40
11. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
At γ1 = 1.2, γ2 = 0.5
At γ1 = 1.2, γ2 = 0.5
Mohamed Mohamed El-Sayed Atyya Page 11 of 40
12. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
Let : G(s) = s+5
s2+2s−3 (unstable), Gm(s) = s+10
s2+8s+10
At γ1 = γ2 = 20
At γ1 = 20, γ2 = 10
Mohamed Mohamed El-Sayed Atyya Page 12 of 40
13. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
At γ1 = 20, γ2 = 40
4. Second Order System Adjustment with First Order Controller
G(s) =
y
u
=
s + b
s2 + a1s + a0
=
N
D
Gm(s) =
ym
uc
=
s + bm
s3 + a2ms2 + a1ms + a0m
Let the control law : u(t) =
t0
1 + r1s
uc(t) −
s0
1 + r1s
y(t)
y =
t0G(s)
1 + r1s
uc −
s0G(s)
1 + r1s
y
y
uc
=
t0N
D(1 + r1s) + s0N
e = y − ym
∂e
∂t0
=
N
D(1 + r1s) + s0N
uc ≈ Gmuc
∂e
∂s0
=
−N2
[D(1 + r1s) + s0N]2
uc ≈ −Gm
y
uc
uc = −Gmy
∂e
∂r1
=
−NDs
[D(1 + r1s) + s0N]2
uc ≈ −Gm
Ds
D(1 + r1s) + s0N
uc
≈ −Gmuc
∂θ
∂t
= −γ
∂e
∂θ
e
∂t0
∂t
= −γ1[Gmuc]e ⇒ t0 = −
γ1
s
[Gmuc]e
∂s0
∂t
= γ2[Gmy]e ⇒ s0 =
γ2
s
[Gmy]e
Mohamed Mohamed El-Sayed Atyya Page 13 of 40
14. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
∂r1
∂t
= γ3[Gmuc]e ⇒ r1 =
γ3
s
[Gmuc]e
Let:
G(s) =
s + 2
s2 + s + 6
Gm(s) =
s + 24
s3 + 9s2 + 26s + 24
Figure 1.5: Second order system adjustment with first order controller block diagram
For Step Input:
γ1 = 50, γ2 = 25, γ3 = −100
Mohamed Mohamed El-Sayed Atyya Page 14 of 40
15. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
For square Input:
γ1 = 7.5, γ2 = 3.75, γ3 = −15
Mohamed Mohamed El-Sayed Atyya Page 15 of 40
16. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
For Sinusoidal Input:
γ1 = 7.5, γ2 = 3.75, γ3 = −15
Mohamed Mohamed El-Sayed Atyya Page 16 of 40
17. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
Mohamed Mohamed El-Sayed Atyya Page 17 of 40
18. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
1.1.2 Determination of Adaptation Gain
• Consider the plant transfer function G(s).
• Multiply the denominator of G(s) by s and add the term µ to get the characteristic equation
sG(s) + µ = 0
where, µ = γymuck.
• Find µ that places all the roots in left half of S − plane.
• If ymuck = constant ⇒ γ =
µ
ymuck
Figure 1.6: Second order system adjustment with first order controller block diagram
Examples
1. First order system
Let :
B = 1, A = s + 1, µ = 1
Mohamed Mohamed El-Sayed Atyya Page 18 of 40
19. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
2. Second order system
Let :
B = 1, A = s2
+ s + 1, µ = 0.4
Mohamed Mohamed El-Sayed Atyya Page 19 of 40
20. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
Mohamed Mohamed El-Sayed Atyya Page 20 of 40
21. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
1.1.3 Normalized MIT Rule
Procedure
Process : G(s) =
y
u
(1.8)
Model : Gm(s) =
ym
uc
(1.9)
Control law : u(t) = f(uc, y) (1.10)
Get closed loop from [1.8] & [1.10] :
y
uc
(1.11)
Error : e = y − ym (1.12)
∂e
∂θ
=
∂y
∂θ
= −ϕ (1.13)
Normalized MIT Rule :
dθ
dt
= γ
ϕe
α + ϕT ϕ
(1.14)
α > 0 (1.15)
Examples
1. Gain Adjustment
Gp(s) = θG(s) = θ
2
s2 + 2s + 4
Gm(s) = θoG(s) = θo
2
s2 + 2s + 4
; θo = 2
e = y − ym = θG(s)uc − θoG(s)uc
ϕ = −
∂e
∂θ
= −G(s)uc = −
ym
θ0
dθ
dt
= γ
ϕe
α + ϕT ϕ
= −γ
yme/θ0
α + (ym/θ0)2
= −γ
yme
α + y2
m
Figure 1.7: Gain adjustment block diagram
Mohamed Mohamed El-Sayed Atyya Page 21 of 40
22. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
At
γ = 1.2, α = 0.1
Mohamed Mohamed El-Sayed Atyya Page 22 of 40
23. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
2. First Order System Adjustment
dy
dt
= −ay + bu
G(s) =
Y
U
=
b
s + a
dym
dt
= −amy + bmu
G(s) =
Ym
U
=
bm
s + am
Use the control law : u(t) = touc(t) − soy(t)
U = toUc − soY = Y
s + a
b
⇒
Y
Uc
=
to
s+a
b + so
=
boto
s + a + bso
bm = bto
am = a + bso
e = Y − Ym =
bto
s + a + bso
Uc =
bm
s + am
Uc
∂e
∂to
=
b
s + a + bso
Uc =
b
s + am
Uc
≈ Gm(s)Uc = ym
∂e
∂so
=
−b2to
(s + a + bso)2
Uc =
−b
s + a + bso
Y =
−b
s + am
Y
≈ −Gm(s)Y
dθ
dt
= γ
ϕe
α + ϕT ϕ
dto
dt
= −γ1
yme
α1 + y2
m
dso
dt
= γ2
Gm(s)ye
α2 + (Gm(s)y)2
Let : a = 1, b = 2, am = 8, bm = 8
Figure 1.8: First order system adjustment block diagram
Mohamed Mohamed El-Sayed Atyya Page 23 of 40
24. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
At
γ1 = 5, γ2 = 10, α1 = 0.1, α2 = 5
Mohamed Mohamed El-Sayed Atyya Page 24 of 40
25. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
3. Second Order System Adjustment
G(s) =
y
u
=
s + b
s2 + a1s + a0
Gm(s) =
ym
uc
=
s + bm
s2 + a1ms + a0m
Let the control law : u(t) = t0uc(t) − s0y(t)
y
s2 + a1s + a0
s + b
= t0uc − s0y
⇒
y
uc
=
t0s + bt0
s2 + (a1 + s0)s + a0 + bs0
e = y − ym
∂e
∂t0
=
s + b
s2 + (a1 + s0)s + a0 + bs0
uc ≈ Gm(s)uc = ym
∂e
∂s0
=
−t0(s + b)(s + b)
[s2 + (a1 + s0)s + a0 + bs0]2
uc
=
−(s + b)
s2 + (a1 + s0)s + a0 + bs0
y =
−y2
t0uc
dθ
dt
= γ
ϕe
α + ϕT ϕ
dt0
dt
= −γ1
yme
α1 + y2
m
ds0
dt
= γ2
y2uc
α2u2
c + y4
Figure 1.9: Second order system adjustment block diagram
Mohamed Mohamed El-Sayed Atyya Page 25 of 40
26. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
Let :
G(s) =
s + 5
s2 + 4s + 3
(stable), Gm(s) =
s + 10
s2 + 8s + 10
, γ1 = 15, γ2 = 0.1, α1 = 15,
α2 = 20
Mohamed Mohamed El-Sayed Atyya Page 26 of 40
27. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
Let :
G(s) =
s + 5
s2 + 2s − 3
(unstable), Gm(s) =
s + 10
s2 + 8s + 10
, γ1 = 400, γ2 = 1,
α1 = α2 = 0.001
Mohamed Mohamed El-Sayed Atyya Page 27 of 40
28. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
4. Second Order System Adjustment with First Order Controller
G(s) =
y
u
=
s + b
s2 + a1s + a0
=
N
D
Gm(s) =
ym
uc
=
s + bm
s3 + a2ms2 + a1ms + a0m
Let the control law : u(t) =
t0
1 + r1s
uc(t) −
s0
1 + r1s
y(t)
y =
t0G(s)
1 + r1s
uc −
s0G(s)
1 + r1s
y
y
uc
=
t0N
D(1 + r1s) + s0N
e = y − ym
∂e
∂t0
=
N
D(1 + r1s) + s0N
uc ≈ Gmuc = ym
∂e
∂s0
=
−N2
[D(1 + r1s) + s0N]2
uc ≈ −Gm
y
uc
uc = −Gmy
∂e
∂r1
=
−NDs
[D(1 + r1s) + s0N]2
uc ≈ −Gm
Ds
D(1 + r1s) + s0N
uc
≈ −Gmuc = −ym
dθ
dt
= γ
ϕe
α + ϕT ϕ
∂t0
∂t
= −γ1
yme
α1 + y2
m
∂s0
∂t
= γ2
Gmye
α2 + (Gmy)2
∂r1
∂t
= γ3
yme
α3 + y2
m
Let:
G(s) =
s + 2
s2 + s + 6
, Gm(s) =
s + 24
s3 + 9s2 + 26s + 24
,
γ1 = 400, γ2 = 33.3333, γ3 = −50, α1 = 100, α2 = 100, α3 = 400
Mohamed Mohamed El-Sayed Atyya Page 28 of 40
29. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
Figure 1.10: Second order system adjustment with first order controller block diagram
Mohamed Mohamed El-Sayed Atyya Page 29 of 40
30. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
Mohamed Mohamed El-Sayed Atyya Page 30 of 40
31. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
1.1.4 Design of MRAS Using Lyapunov Theory
We will now show how Lyapunovs stability theory can be used to construct algorithms for adjusting parameters in adaptive
systems. To do this, we first derive a differential equation for the error, e = y − ym. This differential equation contains the
adjustable parameters. We then attempt to find a Lyapunov function and an adaptation mechanism such that the error will
go to zero.When using the Lyaponov theory for adaptive systems, we find that dV/dt is usually only negative semi-definite.
The procedure is to determine the error equation and a Lyapunov function with a bounded second derivative.
General Case
Given a continuous time system and the target dynamics
˙x = Ax + Bu, ˙xm = Amxm + Bmuc
Consider the controller and the error signals
u(t) = Muc(t) − Lx(t), e(t) = x(t) − xm(t)
If the model-matching problem is solvable, then the error dynamics is
de
dt
= Ax + Bu − Amxm − Bmuc
= Ame + (A − Am − BL) x + (BM − Bm) uc
= Ame + Ψ(x, uc) • θ − θ0
Consider the following Lyapunov function candidate
V =
1
2
eT
Pe +
1
γ
θ − θ0 T
θ − θ0
The time-derivative of V is
˙V =
1
2
eT
PAm + AT
mP e + θ − θ0 T
ΨT
Pe +
1
γ
θ − θ0 T ˙θ
If we solve the Lyapunov equation for P = PT > 0
PAm + AT
mP = −Q, Q > 0
and choose the update law as
˙θ = −γΨT
Pe = −γΨT
(x, uc) • P • (x − xm)
then
˙V = −
1
2
eT
(t)Qe(t)
and we conclude that e(t) → 0
Mohamed Mohamed El-Sayed Atyya Page 31 of 40
32. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
Examples
1. Gain Adjustment
Process : ˙x = −ax + bu
Model : ˙xm = −amxm + bmuc
a = am
Control law : u(t) = m1uc(t)
de
dt
= −ame + (−a + am)x + (bm1 − bm)uc = −ame + (bm1 − bm)uc
= −ame + Ψ(x, uc) • θ − θ0
Ψ =
uc
b
θ = m1
θ0
=
bm
b
˙θ = −γ
uc
b
Pe = −γuce
Figure 1.11: Gain adjustment block diagram
Let
a = am = 4, b = 2, bm = 4, γ = 3
Mohamed Mohamed El-Sayed Atyya Page 32 of 40
33. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
Mohamed Mohamed El-Sayed Atyya Page 33 of 40
34. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
2. First Order System Adjustment
Process : ˙x = −ax + bu
Model : ˙xm = −amxm + bmuc
Control law : u(t) = m1uc(t) − l1x(t)
de
dt
= −ame + (−a + am − bl1)x + (bm1 − bm)uc
= −ame + Ψ(x, uc) • θ − θ0
Ψ =
−x
b
uc
b
θ = [l1 m1]T
θ0
=
am − a
b
bm
b
˙l1
˙m1
=
γ1xe
−γ2uce
Figure 1.12: First order system adjustment block diagram
Let
a = 2, am = 8, b = 1, bm = 8, γ1 = 0.1, γ2 = 3
Mohamed Mohamed El-Sayed Atyya Page 34 of 40
35. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
Mohamed Mohamed El-Sayed Atyya Page 35 of 40
36. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
3. Second Order System Adjustment
Process : G(s) =
s + 5
s2 + 4s + 3
Model : Gm(s) =
s + 10
s2 + 8s + 10
A =
0 1
−3 −4
B =
0
1
Am =
0 1
−10 −8
Bm =
0
1
Control law : u(t) = m1uc − l1 0 x(t)
de
dt
=
0 1
−10 −8
e +
0 0
7 − l1 0
x(t) +
0
m1 − 1
uc
Ψ = [−x uc]
θ = [l1 m1]T
˙l1
˙m1
=
γ1xe
−γ2uce
Figure 1.13: second order system adjustment block diagram
At
γ1 = 0.01, γ2 = 0.4
Mohamed Mohamed El-Sayed Atyya Page 36 of 40
37. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
Mohamed Mohamed El-Sayed Atyya Page 37 of 40
38. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
4. Second Order System Adjustment (another solution)
Process : G(s) =
s + 5
s2 + 4s + 3
Model : Gm(s) =
s + 10
s2 + 8s + 10
A =
0 1
−3 −4
B =
0
1
Am =
0 1
−10 −8
Bm =
0
1
Control law : u(t) = m1uc − l1 l2 x(t)
de
dt
=
0 1
−10 −8
e +
0 0
7 − l1 4 − l2
x(t) +
0
m1 − 1
uc
Ψ = [−x − ˙x uc]
θ = [l1 l2 m1]T
˙l1
˙l2
˙m1
=
γ1xe
γ2 ˙xe
−γ3uce
Figure 1.14: second order system adjustment block diagram
At
γ1 = 0.01, γ2 = 0.8, γ2 = 0.5
Mohamed Mohamed El-Sayed Atyya Page 38 of 40
39. 1.1. MODEL REFERENCE ADAPTATION SYSTEMS (MRAS) Adaptive Control
Mohamed Mohamed El-Sayed Atyya Page 39 of 40
40. 1.2. MATLAB CODES AND SIMULATION Adaptive Control
1.2 MATLAB Codes and Simulation
1 http://goo.gl/2nhYkk
2 http://goo.gl/RqIX4D
3 http://goo.gl/rOCcS6
4 http://goo.gl/Bx5Tnn
1 http://goo.gl/trRRzu
2 http://goo.gl/QrSHSW
1 http://goo.gl/hdrI90
2 http://goo.gl/DDXsR9
3 http://goo.gl/G67l2g
4 http://goo.gl/QJWWvw
1 http://goo.gl/YzgkEL
2 http://goo.gl/86ZvGi
3 http://goo.gl/cSxmF3
4 http://goo.gl/MW7zTw
1.3 References
1. Karl Johan Astrom, Adaptive Control, 2nd
Edition.
2. Leonid B. Freidovich, lecture 12.
1.4 Contacts
mohamed.atyya94@eng-st.cu.edu.eg
Mohamed Mohamed El-Sayed Atyya Page 40 of 40