授業

Advanced Automation

[lecture #1] 2015.9.3 outline of the lecture, review of classical and modern control theory (1/3)

%-- 9/3/2015 2:09 PM --%
s = tf('s')
Ptf = 1/(s+1)
pole(Ptf)
impulse(Ptf)
Pss = ss(Ptf)
initial(Pss, 1)
initial(Pss, 2)

fileex0902.m

[lecture #2] 2015.9.10 review of classical and modern control theory (2/3) with introduction of Matlab/Simulink

  1. introduction of Matlab and Simulink filetext_fixed.pdf Basic usage of MATLAB and Simulink used for 情報処理演習及び考究II/Consideration and Practice of Information Processing II: Advanced Course of MATLAB
    • interactive system (no compilation, no variable difinition)
    • m file
  2. system representation: Transfer Function(TF) / State-Space Representation (SSR)
    • example: mass-spring-damper system
    • difinition of SSR
    • from SSR to TF
    • from TF to SSR: controllable canonical form
  3. open-loop characteristic
    • open-loop stability: poles and eigenvalues
    • Bode plot and frequency response fileex0910_1.m filemod0910_1.mdl
      • cut off frequency; DC gain; -40dB/dec; variation of c
      • relation between P(jw) and steady-state response
  4. closed-loop stability
    • Nyquist stability criterion (for L(s):stable)
    • Nyquist plot fileex0910_2.m filemod0910_2.mdl
      • Gain Margin(GM); Phase Margin(PM)
%-- 9/10/2015 1:55 PM --%
ex0910_1
P
P.den
P.den{:}
P.num{:}
ex0910_1
ex0910_2

[] 2015.9.17 cancelled

[] 2015.9.25 no lecture (lectures for Monday are given)

[lecture #3] 2015.10.1 review of classical and modern control theory (3/3)

  1. LQR problem
    • controllability
    • cost function J >= 0
    • (semi)-positive definiteness
  2. solution of LQR problem
    • ARE and quadratic equation
    • closed loop stability ... Lyapunov criterion
    • Jmin filelqr.pdffileproof4.pdf (from B3「動的システムの解析と制御」)
  3. example filemod1001.mdl
    A = [1, 2; 0, -1]; % unstable plant
    B = [0; 1];
    Uc = ctrb(A,B);
    det(Uc) % should be nonzero
    C = eye(2); % dummy
    D = zeros(2,1); % dummy
    F = [0, 0]; % without control
    x0 = [1; 1]; % initial state
    Q = eye(2);
    R = 1;
    P = are(A, B/R*B', Q);
    eig(P) % should be positive
    F = R\B'*P;
    x0'*P*x0
%-- 10/1/2015 2:08 PM --%
mod1001
A = [1, 2; 0, -1]; % unstable plant
B = [0; 1];
Uc = ctrb(A,B);
A
B
Uc
det(Uc)
C = eye(2); % dummy
D = zeros(2,1); % dummy
F = [0, 0]; % without control
x0 = [1; 1]; % initial state
Q = eye(2);
R = 1;
F
P = are(A, B/R*B', Q);
P
eig(P)
F = R\B'*P;
F
J
x0
x0'*P*x0
A-B*F
eig(A-B*F)

... I'm sorry but all of equations are in the pdf file.

[lecture #4] 2015.10.8 relation between LQR and H infinity control problem (1/2)

  1. an equivalent problem
  2. a simple example of state-feedback H infinity control problem
  3. definition of H infinity norm (SISO)
    s = tf('s');
    P1 = 1/(s+1);
    bode(P1);
    norm(P1, 'inf')
    P2 = 1/(s^2 + 0.1*s + 1);
    bode(P2);
    norm(P2, 'inf')
  4. definition of H infinity norm (SIMO)
  5. solve the problem by hand
  6. solve the problem by tool(hinfsyn) fileex1008.m
%-- 10/8/2015 1:48 PM --%
s = tf('s');
P1 = 1/(s+1);
bode(P1);
norm(P1, 'inf')
P2 = 1/(s^2 + 0.1*s + 1);
bode(P2);
norm(P2, 'inf')
ex1008

[lecture #5] 2015.10.15 relation between LQR and H infinity control problem (2/2)

  1. complete the table in simple example
  2. behavior of hinfsyn in fileex1008.m
  3. confirm the cost function J for both controllers by simulation filemod1015.mdl
  4. confirm the closed-loop H infinity norm for both controllers by simulation (common mdl file is available)
    • review: steady-state response (see photo 8 @ lec. #2)
    • how to construct the worst-case disturbance w(t) which maximizes L2 norm of z(t) ?
    • what is the worst-case disturbance in the simple example ?
  5. general case: filehinf.pdf includes the simple example as a special case
    • LQR filelqr.pdf is included as a special case where gamma -> infinity, non-zero x(0), and B2 -> B
%-- 10/15/2015 1:14 PM --%
ex1008
K
dcgain(K)
gopt
ex1008
mod1015
f
f = 1
x0 = 0
h = 0.1
zz
zz(end)
h = 1e-6
zz(end)
f = -1+sqrt(2)
h
zz(end)
x0 = 1
zz(end)
f
h
h = 10
zz(end)/ww(end)
x0
x0 = 0
zz(end)/ww(end)
sqrt(zz(end)/ww(end))
h
h = 100
sqrt(zz(end)/ww(end))

[lecture #6] 2015.10.22 Mixed sensitivity problem 1/3

  1. review filemap_v1.0_intro1.pdf and outline
  2. H infinity control problem (general form)
  3. reference tracking problem
  4. weighting function for sensitivity function
  5. design example fileex1022_1.m fileex1022_2.m
  6. the small gain theorem
    • proof: Nyquist stability criterion
  7. from performance optimization to robust stabilization
%-- 10/22/2015 2:06 PM --%
ex1022_1
eig(P)
ex1022_2

[lecture #7] 2015.10.29 Mixed sensitivity problem 2/3

  1. review filemap_v1.0_intro2.pdf and outline
  2. an equivalent problem of robust stabilization for reference tracking problem
  3. uncertainty model and normalized uncertainty Delta
  4. robust stabilization problem and an equivalent problem
  5. practical example of plant with perturbation fileex1029_1.m
  6. how to determine the model fileex1029_2.m
  7. design example and simulation fileex1029_3.m filemod1029.mdl
%-- 10/29/2015 1:52 PM --%
ex1029_1
ex1029_2
ex1029_3
mod1029
c
c = 0.8

[lecture #8] 2015.11.5 Mixed sensitivity problem 3/3

  1. review : (1)robust stabilization and (2)performance optimization
  2. mixed sensitivity problem : a sufficient condition for (1) and (2)
    • proof by definition of H infinity norm
  3. construction of the generalized plant
  4. design example fileex1105_1.m
  5. gamma iteration by bisection method fileex1105_2.m
  6. a problem of the mixed sensitivity problem: nominal performance and robust performance fileex1105_3.m
  7. introduction of robust performance problem
%-- 11/5/2015 1:37 PM --%
ex1105_1
ex1105_2
gam
ex1105_2
WT
ex1105_2
ex1105_3
ex1105_2
ex1105_3

[lecture #9] 2015.11.12 robust performance problem 1/3

  1. review: robust performance problem
  2. an equivalent robust stability problem
  3. definition of H infinity norm for general case (MIMO)
  4. definition of (maximum) singular value
    M = [j, 0; -j, 1]
    M'
    eig(M'*M)
    svd(M)
  5. mini report #1 filereport1.pdf
    • write by hand
    • due date and place of submission -> see schedule2015
    • check if your answer is correct or not before submission by using Matlab
    • You will have a mini exam #1 related to this report
  6. SVD: singular value decomposition
    • definition
      [U,S,V] = svd(M)
      M = [j, 0; -j, 1; 2, 3]
    • unitary matrix and 2 norm of vectors
    • a property of SVD: input-output interpretation
    • illustrative example: rotation matrix fileex1112_1.m
  7. H infinity norm of Delta hat
%-- 11/12/2015 1:01 PM --%
M = [j, 0; -j, 1]
M'
eig(M'*M)
svd(M)
M = [j, 0; -j, 1]
M'
eig(M'*M)
svd(M)
(3+sqrt(5))/2
sqrt((3+sqrt(5))/2)
help svd
[U,S,V] = svd(M)
U'*U
M = [j, 0; -j, 1; 2, 3]
[U,S,V] = svd(M)
V'*V
ex1112_1

[lecture #10] 2015.11.19 Robust performance problem (2/3)

  1. return of mini report #1
  2. review and outline: robust stability problem for Delta hat and its equivalent problem(?)
  3. signal vector's size is not restricted in H infinity control problem and small gain theorem
  4. H infinity norm of Delta hat
  5. design example: robust performance is achieved fileex1119_1.m
  6. non structured uncertainty is considered ... the design problem is too conservative
%-- 11/19/2015 1:23 PM --%
doc hinfsyn
ex1105_2
ex1105_3
gam_opt
ex1119_1
gam_opt
svd([1/sqrt(2), 0; 1/sqrt(2), 0])

[lecture #11] 2015.11.26 Robust performance problem (3/3)

  1. review
    • robust performance problem with Delta hat and conservative design problem with Delta tilde
    • inclusion relation between two uncertain sets
  2. introduction of the scaled H infinity control problem
  3. how to determine structure of scaling matrix
  4. design example moved to next lecture
    % less conservative design 
    ex1105_2
    ex1105_3
    ex1119_1
    gam_opt0 = gam_opt;
    K_opt0 = K_opt;
  5. effect of scaling matrix moved to next lecture
  6. mini exam #1

[lecture #12] 2015.12.3 Robust performance problem (1/3) (cont.), Control system design for practical system (1/3)

  1. return of mini exam #1; schedule of mini report #2 and exam #2
  2. review of the scaled H infinity control problem
  3. comments on mu-synthesis prolem
  4. design example (moved from the previous lecture)
    % less conservative design 
    ex1105_2
    ex1105_3
    ex1119_1
    gam_opt0 = gam_opt;
    K_opt0 = K_opt;
  5. effect of scaling matrix (moved from the previous lecture)
  6. mini report #2 filereport2.pdf
    • write by hand
    • due date and place of submission -> see schedule2015
    • check if your answer is correct or not before submission by using Matlab
    • You will have a mini exam #2 related to this report
  7. controller design for practical system: active noise control in duct
    • introduction of experimental setup
    • objective of control system: to drive control loudspeaker by generating proper driving signal u using reference microphone output y such that the error microphone's output z is attenuated against the disturbance input w
    • frequency response experiment

!!! the remaining page is under construction (the contents below are from 2014) !!!

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[lecture #12] 2014.12.4 relation between H infinity control and modern control theory (cont.); Speed control of two inertia system with servo motor (1/4)

%-- 12/4/2014 1:28 PM --%
ex1127
mod1127
x0 = 0
h = 1
f = 1
ww
zz
h = 10
ww
zz
h = 50
zz
h
zz

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[lecture #13] 2014.12.11 Robust control design for a practical system : Speed control of two inertia system with servo motor (1/3)

%-- 12/11/2014 1:24 PM --%
ex1211_1
frdata
frdata(:,1)
P1_jw
P1_g
ex1211_1

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[lecture #14] 2014.12.18 Robust control design for a practical system : Speed control of two inertia system with servo motor (2/3)

  1. design your controller(s) so that the system performance is improved compared with the design example above
  2. Draw the following figures and explain the difference between two control systems (your controller and the example above):
    1. bode diagram of controllers
    2. gain characteristic of sensitivity function
    3. time response of control experiment
  3. Why is the performance of your system improved(or unfortunately deteriorated)?
    • due date: 9th(Fri) Jan 17:00
    • submit your report(pdf or doc) by e-mail to kobayasi@nagaokaut.ac.jp
    • You can use Japanese
    • maximum controller order is 20
    • submit your cont.dat, cont_order.dat, and cont.mat to kobayasi@nagaokaut.ac.jp not later than 26th(Fri) Dec

participant list2014

%-- 12/18/2014 1:01 PM --%
freqresp
nominal
help fitfrd
weight
cont
help c2d
perf

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[lecture #15] 2014.12.25 Robust control design for a practical system : Speed control of two inertia system with servo motor (3/3)

%-- 12/25/2014 12:58 PM --%
load cont.mat
who
K_opt
who
Kd
who
Ghat
load result.dat
plot(result(:,1), result(:,2))
plot(result(:,1), result(:,3))
who
bode(K_opt)
bode(Kd)
Kd1 = Kd
K_opt1 = K_opt
load cont.mat
bode(K_opt1, 'b', K_opt, 'r')
bode(Kd1, 'b', Kd, 'r')
Kd_tmp = c2d(K_opt1, 0.000001);
bode(Kd1, 'b', Kd, 'r', Kd_tmp, 'm')
clear all
load cont.mat
who
bode(K_opt)
K_example = K_opt;
load cont.mat
bode(K_example, 'b', K_opt, 'r')

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