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Polcz Péter honlapja

Tartalomjegyzék

LTI controllability observability

Teljes Matlab script kiegészítő függvényekkel.

Check controllability and observability of an LTI system using LMI conditions

File: LTI_ctrb_obsv.m
Directory: 2_demonstrations/workspace/ccs/ccs_2018
Author: Peter Polcz (ppolcz@gmail.com)
Created on 2018. November 06.

Generate an unstable non-minimum phase MIMO LTI system

a = 4;        % Nr. of contr. and obs.
b = 2;        % Nr. of contr. and unobs.
c = 1;        % Nr. of uncontr. and obs.
d = 2;        % Nr. of uncontr. and unobs.
n = a+b+c+d;  % Nr. of states
m = 2;        % Nr. of inputs
p = 3;        % Nr. of outputs
is_stabilisable = 0;

[A,B,C,D] = LTI_generate_sys(a,b,c,d,m,p,is_stabilisable);

pcz_display(A,B,C,D)
Output:
A [9x9] = 
 
  Columns 1 through 7

   -0.3854   -0.1115   -0.8659    0.9982    0.1392   -0.1227   -0.5507
    0.3527   -0.3931   -0.2876    0.0364   -0.3432    0.2381   -0.2856
    0.3185    1.5245    1.0902    0.1159    1.3377    0.2412   -0.4362
   -0.7679    0.6399    0.7834   -0.6353   -0.3211   -0.9986   -0.4801
   -0.5263   -1.8969   -0.3223   -1.4173   -1.7810    0.2332    0.2312
    0.7449   -0.6363    0.0445   -0.1514   -0.3093   -0.7250    0.7354
   -0.3823   -1.2174    0.8619    0.3559    0.6969    0.2617    1.4206
    0.8841    1.6209   -1.1647    1.1125    0.5730   -1.0474   -1.1842
    0.5016   -0.4082   -0.5664    1.0788    0.0259   -1.0732    0.2587

  Columns 8 through 9

    0.5190    0.4326
   -0.2757   -0.2659
    0.3040    0.4320
   -0.6928   -0.0070
   -0.9877   -0.6107
   -0.4421    0.0664
    0.6699    0.3284
    0.2541    0.7315
    0.1711   -0.5239

 
B [9x2] = 
 
   -0.6677    1.0997
   -0.0440    0.1737
   -0.6871   -0.6192
   -0.9191    0.2573
    0.5206    0.9395
   -0.4330    1.8049
    0.6751   -0.0365
   -0.3556   -1.2189
   -0.1271    0.6115

 
C [3x9] = 
 
  Columns 1 through 7

   -1.5725   -0.0285   -1.0454   -0.7619    0.2302   -1.1239    1.3237
    1.0396    0.4461    0.4953    0.5334   -0.1662    0.1683   -0.3672
   -0.0657    0.7354    0.2316    0.0232    0.1102   -0.4217    0.1611

  Columns 8 through 9

    1.3796    0.4617
   -0.8704    0.6409
   -0.0781    0.5073

 
D [3x2] = 
 
     0     0
     0     0
     0     0

 

Compute the uncontrollable eigenvalues

Check wether the system is stabilisable.

Cn = ctrb(A,B);

[U,Sigma,V] = svd(Cn);

T = U';

A_Ctrb_Staircase = round(T*A*T',10)
B_Ctrb_Staircase = round(T*B,10)

k = c+d-1;
A_Unctrb = A_Ctrb_Staircase(end-k:end,end-k:end)

Uncontrollable_Eigenvalues = eig(A_Unctrb)

Check wether the system is detectable.

On = obsv(A,C);

[U,Sigma,V] = svd(On);

T = V';

A_Obsv_Staircase = round(T*A*T',10)
C_Obsv_Staircase = round(C*T',10)

k = b+d-1;
A_Unobsv = A_Obsv_Staircase(end-k:end,end-k:end)

Unobservable_Eigenvalues = eig(A_Unobsv)
Output:
A_Ctrb_Staircase =

  Columns 1 through 7

   -1.9598    1.1465   -0.2252    0.7011    0.2094    0.4224    1.6623
   -0.3071    0.6537   -1.8117   -0.6719    0.0106   -0.3603    2.3914
   -0.0673    0.8104    0.0956   -0.6325   -1.6739   -1.0993    0.0284
   -0.0318    0.0655   -0.0627   -0.6385    0.9473    1.5515    1.4845
    0.0094   -0.0190    0.0170    0.1275   -0.7077    0.5763    0.5926
    0.0028   -0.0064    0.0062    0.0336   -0.2870    0.4305    0.1154
         0         0         0         0         0         0    0.6165
         0         0         0         0         0         0    0.4374
         0         0         0         0         0         0    0.1456

  Columns 8 through 9

   -1.1410   -1.2883
   -1.2424   -0.2010
    0.1954    0.2247
   -0.7585   -0.0685
   -0.5822   -0.2886
    0.0241    0.0826
    0.3062    2.5902
   -0.3872    0.1614
   -0.3808    0.2182


B_Ctrb_Staircase =

   -0.1374   -1.6309
   -1.0618    0.0152
    0.6935    0.6369
   -0.5930   -0.9557
    0.4561   -1.2795
   -0.7966    1.4403
         0         0
         0         0
         0         0


A_Unctrb =

    0.6165    0.3062    2.5902
    0.4374   -0.3872    0.1614
    0.1456   -0.3808    0.2182


Uncontrollable_Eigenvalues =

   -0.8166
    0.7065
    0.5575


A_Obsv_Staircase =

  Columns 1 through 7

    0.1586   -0.9420    0.0117   -0.0009   -0.0149         0         0
    1.4624    0.4363    0.0625    0.0313    0.0037         0         0
   -0.0182    0.1673   -1.1065   -0.0118    0.0434         0         0
   -0.3312   -1.7904    0.3777    0.2239   -0.0169         0         0
    1.0048    1.2020   -0.4829    0.3304   -0.7166         0         0
    0.2582    0.0235    0.2845    0.6016    0.4536   -1.7981    0.0095
   -0.0123   -0.2227   -0.5979   -0.2324    0.1160    0.6361    0.9252
   -0.1025    0.5023    1.2264    0.1758   -0.5110   -0.7362   -1.9214
   -0.5494    1.0979    2.2412   -1.2708   -1.9260   -0.9490    1.7835

  Columns 8 through 9

         0         0
         0         0
         0         0
         0         0
         0         0
    0.0028    0.0012
    0.0140   -0.0108
    0.2743    0.0040
   -2.3174   -0.0757


C_Obsv_Staircase =

  Columns 1 through 7

   -2.0664    0.2258   -0.1648    2.2170   -0.2618         0         0
    0.7404   -0.1379    0.0075   -1.2983   -0.9570         0         0
   -0.2656    0.5642    0.1135   -0.0946   -0.8173         0         0

  Columns 8 through 9

         0         0
         0         0
         0         0


A_Unobsv =

   -1.7981    0.0095    0.0028    0.0012
    0.6361    0.9252    0.0140   -0.0108
   -0.7362   -1.9214    0.2743    0.0040
   -0.9490    1.7835   -2.3174   -0.0757


Unobservable_Eigenvalues =

   -1.7987
    0.7065
    0.5575
   -0.1396

Compute a stabilising static state feedback gain with LMI

Q = sdpvar(n,n,'symmetric');
N = sdpvar(m,n,'full');

CONS = [
    Q - 0.01*eye(n) >= 0
    Q*A' + A*Q - B*N - N'*B' + 0.01*eye(n) <= 0
    ];

sol = optimize(CONS)

Q = double(Q);
N = double(N);
P = inv(Q);
K = N/Q;

Check solution

Eigenvalues_of_the_closed_loop = eig(A - B*K)
Output:
Problem
  Name                   :                 
  Objective sense        : min             
  Type                   : CONIC (conic optimization problem)
  Constraints            : 63              
  Cones                  : 0               
  Scalar variables       : 0               
  Matrix variables       : 2               
  Integer variables      : 0               

Optimizer started.
Presolve started.
Linear dependency checker started.
Linear dependency checker terminated.
Eliminator - tries                  : 0                 time                   : 0.00            
Lin. dep.  - tries                  : 1                 time                   : 0.00            
Lin. dep.  - number                 : 0               
Presolve terminated. Time: 0.00    
Problem
  Name                   :                 
  Objective sense        : min             
  Type                   : CONIC (conic optimization problem)
  Constraints            : 63              
  Cones                  : 0               
  Scalar variables       : 0               
  Matrix variables       : 2               
  Integer variables      : 0               

Optimizer  - threads                : 4               
Optimizer  - solved problem         : the primal      
Optimizer  - Constraints            : 63
Optimizer  - Cones                  : 0
Optimizer  - Scalar variables       : 0                 conic                  : 0               
Optimizer  - Semi-definite variables: 2                 scalarized             : 90              
Factor     - setup time             : 0.00              dense det. time        : 0.00            
Factor     - ML order time          : 0.00              GP order time          : 0.00            
Factor     - nonzeros before factor : 2016              after factor           : 2016            
Factor     - dense dim.             : 0                 flops                  : 2.16e+05        
ITE PFEAS    DFEAS    GFEAS    PRSTATUS   POBJ              DOBJ              MU       TIME  
0   1.8e+00  1.0e+00  8.2e-01  0.00e+00   -1.800000000e-01  0.000000000e+00   1.0e+00  0.00  
1   3.2e-01  1.9e-01  3.0e-01  9.00e-01   -8.174551064e-02  0.000000000e+00   1.8e-01  0.01  
2   2.9e-02  1.7e-02  4.2e-02  5.85e-01   -8.305168886e-02  0.000000000e+00   1.7e-02  0.01  
3   2.6e-04  1.5e-04  5.0e-05  -6.13e-01  -5.818354522e+00  0.000000000e+00   1.5e-04  0.01  
4   1.4e-06  7.7e-07  1.8e-08  -1.00e+00  -1.259788337e+03  0.000000000e+00   7.6e-07  0.01  
5   4.1e-08  2.3e-08  8.6e-11  -1.02e+00  -4.801030346e+04  0.000000000e+00   2.3e-08  0.01  
6   2.0e-09  1.3e-09  9.0e-13  -1.02e+00  -1.043649970e+06  0.000000000e+00   1.1e-09  0.01  
7   6.6e-11  2.8e-09  5.9e-15  -1.01e+00  -2.954177094e+07  0.000000000e+00   3.8e-11  0.01  
8   4.7e-12  3.2e-07  1.1e-16  -9.81e-01  -3.953550118e+08  0.000000000e+00   2.7e-12  0.01  
Optimizer terminated. Time: 0.02    


MOSEK DUAL INFEASIBILITY REPORT.

Problem status: The problem is dual infeasible

Interior-point solution summary
  Problem status  : DUAL_INFEASIBLE
  Solution status : DUAL_INFEASIBLE_CER
  Primal.  obj: -5.2587724064e-02   nrm: 1e+00    Viol.  con: 2e-11    barvar: 0e+00  
Optimizer summary
  Optimizer                 -                        time: 0.02    
    Interior-point          - iterations : 8         time: 0.02    
      Basis identification  -                        time: 0.00    
        Primal              - iterations : 0         time: 0.00    
        Dual                - iterations : 0         time: 0.00    
        Clean primal        - iterations : 0         time: 0.00    
        Clean dual          - iterations : 0         time: 0.00    
    Simplex                 -                        time: 0.00    
      Primal simplex        - iterations : 0         time: 0.00    
      Dual simplex          - iterations : 0         time: 0.00    
    Mixed integer           - relaxations: 0         time: 0.00    


sol = 

  struct with fields:

    yalmiptime: 0.2210
    solvertime: 0.0275
          info: 'Infeasible problem (MOSEK)'
       problem: 1


Eigenvalues_of_the_closed_loop =

  -0.5157 + 2.5562i
  -0.5157 - 2.5562i
   0.7068 + 0.0000i
   0.5573 + 0.0000i
  -0.6098 + 0.9554i
  -0.6098 - 0.9554i
  -1.4739 + 0.0000i
  -0.6405 + 0.0000i
  -0.8166 + 0.0000i