Documentation of res_auto_good_1

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Function Synopsis

Results = augood1(what) 

Help text

 different best results from optimizations
 discrete simulation:

Listing of function res_auto_good_1

function Results = augood1(what) 

   % jochen's preliminary results; y0 = 1; v = 20
   % CP = [1.7396, -3.2512, 1.5255, 0.2307, 0.0473];
   % ObjVal = depends;

   if what == 0, what = 1:10; end

   Results = [];
   
   % y0 = 1; v = 20
   if any(what == 1),
   Results = [Results;
              0.16295627544389, -0.32441198372221, 0.16145634113975, -0.48246165373014, 0.23211641118531;    % 1. very, very good !!!
              0.45939157981585, -0.91869233504445, 0.45930134556161, -0.61461526108800, 0.04126768355127;    % 2. 
              0.45928272442891, -0.91856022957996, 0.45927773359010, -0.59179033205308, 0.03906058541230;    % 3. 
              0.53159072169012  -1.06303517555409   0.53144458661858  -0.59169691008022 -0.03015406834300;   % 4. m2120101.m
              0.16244129131303  -0.32393514656401   0.16149448811241  -0.43363648606359  -0.02861214407989;  % 5. M215010b, the best  set2
              0.70048369821448  -1.39842816302697   0.69794512155085  -0.51448441780678  -0.06347291367425;  % 6. M2120102
              0.32047887681357  -0.64087619342424   0.32039734872873  -0.66326877445118   0.00175791476137;  % 7. M212010l
              0.32113500474326  -0.64214076556780   0.32100579294260   0.32970073848828   0.03490954134828;  % 8. M212010l too
              0.32113500474326  -0.64214076556780   0.32100579294260  -0.17205783695118  -0.18713378906250;  % 9. M212010l too
              0.32041211961142  -0.64080943622210   0.32039734872873  -0.55429431766407   0.01520853731996;  % 10. M212010m     set3
              0.32076688645712  -0.64091815509417   0.32015320810373  -0.68136569827930   0.02614336703188;  % 11. M212010l IndAll300   set1
              0.16020605980571  -0.32040471811105   0.16019867436437  -0.27714715883203   0.00760426865998;  % 12. set3./2 
             ];
      % ObjVal's
      % v = 20, y0 = 1.0:  1.0e+005 * 0.10057119193979   0.47556072978714   0.47879737304997   0.64975836659065  0.07478344513659   1.21455065136055  0.212174008142268   0.630810190912037
      % v = 30, y0 = 1.0:  1.0e+005 * 0.17983025146276   1.46286714171707   1.42881826268818   2.21942480432239  0.14643012752156   4.52921620046955  0.626177536891911   1.416070401065340
      % v = 40, y0 = 1.0:  1.0e+010 * 0.00000320248197   0.00006196663494   0.00005288911719   0.00030744340929  0.00000270473472   1.01848987201270  0.000019744767579   0.000025287341709
      % v = 50, y0 = 1.0:  1.0e+010 * 0.00000551015376   1.04343315894068   1.03305498425895   1.07972195547967  0.00000472055118   1.11906054589667  1.009079743954618   0.000040048755757
      % v = 60, y0 = 1.0:  1.0e+010 * 0.00000937829943   1.13322856936063   1.12341323642100   1.17109718154814  0.00000810491223   1.22726255686572
      % v = 70, y0 = 1.0:  1.0e+010 * 0.00001675179770   1.22570556485178   1.21635986357337   1.26712371039554  0.00001472066804   1.34095394406603                      0.000085957222690
      % v = 80, y0 = 1.0:  1.0e+010 * 0.00003571488295   1.32033608073346   1.31139458447802   1.36674685739259  0.00003531502092   1.45885398675137                      0.000126647028842
      % v = 90, y0 = 1.0:  1.0e+010 * 0.00019715138866   1.41675236346460   1.40816834133550   1.46922083485636  1.00682876651985   1.58005908800884
      % v =100, y0 = 1.0:  1.0e+010 * 1.05387564291298   1.51464831491000   1.50638833275305   1.57396927976604  1.05910187196915   1.70388154359609                      0.000517197780256
   end
      
   % y0 = 1; v = 100
   if any(what == 2),
   Results = [Results;
              0.11571506450639, -0.23027288460112, 0.11458134113975, -0.04408073425293, 0.11643953130250;  % ObjVal = 2.209357496896689e+004;
              0.11355785320268, -0.21599065803862, 0.10435032307334, -0.04603385925293, -0.05971937372680  % ObjVal = 1.801678158603746e+005;  instabil???
             ];
   end

   % y0 = 1; v = 50
   if any(what == 3),
   Results = [Results;
              0.16253856609330, -0.32399999641752, 0.16146206318565, -0.42761775114225, 0.49502725408082;
              0.16253856609330, -0.32399999641752, 0.16155933796592, -0.45103999235319, 0.27517862126832;
             ];
      % ObjVal's
      % v = 20, y0 = 1.0:  1.0e+010 * 0.00000116508535   1.00440152571050
      % v = 30, y0 = 1.0:  1.0e+010 * 0.00000267032555   1.00157979683025
      % v = 40, y0 = 1.0:  1.0e+004 * 4.95107131217169   4.08163971053753
      % v = 50, y0 = 1.0:  1.0e+004 * 8.64530422754867   6.09397027452597
      % v = 60, y0 = 1.0:  1.0e+005 * 1.48595750402870   0.98812346953832
      % v = 70, y0 = 1.0:  1.0e+005 * 2.67350931654530   1.69618619214333
      % v = 80, y0 = 1.0:  1.0e+005 * 5.74608864413342   3.34079677525641
      % v = 90, y0 = 1.0:  1.0e+006 * 3.08451400189324   1.12576536284787
      % v =100, y0 = 1.0:  1.0e+010 * 1.05485407677142   1.03670591721521
   end

   % y0 = 0.5; v = 50
   if any(what == 4),
   Results = [Results;
              1.41996319061501, -2.34916129874019, 0.99842493037756,  0.86642667924365, 0.27121687195827;  % ObjVal = 3.683564263641345e+006;
              1.14007132190504, -1.95043769644527, 0.92975465755041,  0.40753004228076, -0.01973229772495; % ObjVal = 1.594380021947799e+006;
             ];
   end

   % best from LQG design: 0.0503, -0.0992, 0.0489, 0.0477, 0.0096    discret, minreal
   % yc = 0.5; v = 50;
   % best with small para's: 0.00089059479414, -0.00094354981267, 0.00003741341071, -0.00151211742805, -0.00192357877415   % ObjVal = 17.60847483582232;
   %       15s simulation   -0.00039817588706   0.00117003323841  -0.00077644825100  -0.00200000000000  -0.00200000000000  % ObjVal = 28.52624213227640

   % y0 = 0.5; v = 50; wuout = 6.25e4
   if any(what == 5),
   Results = [Results;
              0.72696206947480  -1.28745740193246   0.60939644307194   0.14544136160719  -0.00667664420773;  % ObjVal = 3.942511580937036e+007
              0.72704790016328  -1.29245465535042   0.61187599629460   0.14544136160719  -0.05553337943723;  % ObjVal = 3.986095133148434e+007
              0.33796005199537  -0.64031145334274   0.31346152316674  -0.23599164797568  -0.07489893796694;  % ObjVal = 5.857916605834016e+006
              0.38865450186951  -0.69712222883068   0.32760601622009  -0.20618107405036   0.03145769042306;  % ObjVal = 7.837765551029298e+006
             ];
   end

   % y0 = 0.5; v = 20; wuout = 6.25e4
   if any(what == 6),
   Results = [Results;
              2.07357656508461  -3.88871832824137   1.88827468246748  -0.24862324293408  -0.04613891997003;  % ObjVal = 3.427209911708983e+007
             ];
   end

   % y0 = 0.5; v = 50; wuout = 6.25e2
   if any(what == 7),
   Results = [Results;
              0.44831731221849  -0.85491374009286   0.41760980455695  -0.11615344106308  -0.08993062391867;  % ObjVal = 1.288587876787554e+005
             ];
   end

   % coupled parameters, y0 = 1.0; v = 20/10; wuout = 6.25e2, wyoutnot0 = 1.0e5
   if any(what == 8),
   Results = [Results;
              0.03474493681250   0.00000268521199  -0.00000268412865  -0.88313348655029   0.02436119461591;  % m4120101
              0.21275758620246   0.00045222017609  -0.00045222005129  -0.91274261474609   0.16270369811740;  % m4110101
             ];
   end


   % method 1, conmtinous system, y0 = 1.0; v = 20; wuout = 6.25e2, wyoutnot0 = 1.0e5
   if any(what == 9),
   Results = [Results;
              0.53192084893716   0.00000157229081   0.00000006273051  55.89398384094238  60.32616179919131;  % m1110101
              0.08219352759548   0.00000139749580   0.00000007417537  43.45576279351954  41.16091800144800;  % m1120101
              0.110993984277     0.000116323321     0.000000457764    67.583414619199    100.000000000000;   % m1130101
              0.20811698370983   0.00000087372983   0.00000007680173  75.16372605674695  90.46620368957520;  % m1120102   21.01.95   ObjVal = 1.458734271267636e+003
uout1_10 = (every 1)
  -0.20811698370983
  -0.14273550514757
  -0.09756653638996
  -0.04480757036067
  -0.00763391338918
   0.00267803322562
   0.00304529168661
   0.00266975387029
   0.00232646595786
   0.00201522321299
uout11_e = (every 5)
   0.00173419551860
   0.00071643390999
   0.00018078639870
  -0.00005965408677
  -0.00013783173576
  -0.00013822632865
  -0.00010916935648
  -0.00007513196467
  -0.00004640592833
  -0.00002575482485
  -0.00001251592599
  -0.00000487287744
yout1_40 = (every 1)
   1.00000000000000
   0.99999925529968
   0.99999453722252
   0.99996277187027
   0.99977401829465
   0.99881590296478
   0.99460510433997
   0.97800555723019
   0.95230845928231
   0.91944129390714
   0.88107888212671
   0.83866743005243
   0.79344714248763
   0.74647337672475
   0.69863633380591
   0.65067930305253
   0.60321549073036
   0.55674347573009
   0.51166134450186
   0.46827956453888
   0.42683266078110
   0.38748976269271
   0.35036409171511
   0.31552145953927
   0.28298784738328
   0.25275613538148
   0.22479204945091
   0.19903939073583
   0.17542461006428
   0.15386078688446
   0.13425106897401
   0.11649162590730
   0.10047416588957
   0.08608806217543
   0.07322213192818
   0.06176610708063
   0.05161183355779
   0.04265423213911
   0.03479205128977
   0.02792843949028
yout41_e = (every 5)
   0.02197136194872
   0.00292678575691
  -0.00426009092288
  -0.00545217987431
  -0.00412975707367
  -0.00213814715347
            % 0.14608923455387  -0.29217846296422   0.14608922841574  -1.46523871020051   0.47159379838356;  % discrete system zu obigem vom 21.01.95, c2dm mit t=0.01s und 'foh'
uout1_100 = (every 10)
  -0.14608923455387
   0.00308487636758
   0.00293110445154
   0.00269904014023
   0.00247997946545
   0.00227357971704
   0.00207942428273
   0.00189708298460
   0.00172611581050
   0.00156607623734
uout101_e = (every 50)
   0.00141651420679
   0.00081017765216
   0.00040010524331
   0.00013731241655
  -0.00001938015844
  -0.00010284771686
  -0.00013820658536
  -0.00014387458246
  -0.00013280780770
  -0.00011371185586
  -0.00009211993219
  -0.00007129237890
  -0.00005293039790
  -0.00003771831888
  -0.00002571909944
  -0.00001665094731
  -0.00001007195653
  -0.00000549645941
  -0.00000246272671
yout1_100 = (every 10)
   1.00000000000000
   0.99807194741342
   0.99148344470920
   0.98070928913750
   0.96628194088426
   0.94869127585838
   0.92838695215112
   0.90578076266803
   0.88124890793865
   0.85513418684197
yout101_e = (every 50)
   0.82774810368899
   0.68070698263935
   0.53391264394792
   0.40169478722968
   0.29042688131786
   0.20152037787877
   0.13354429905480
   0.08365974933704
   0.04854002914776
   0.02491995279496
   0.00988909344980
   0.00101665556306
  -0.00362767632318
  -0.00551161742611
  -0.00570643421937
  -0.00496199152640
  -0.00377926294376
  -0.00247432526977
  -0.00123141693184
ObjVal = 1.042181909982872e+003

             ];
   end

   % good control vector
   % uout = [repmat(-0.00009,[1,1]) repmat(0,[1,5]) repmat(0.000017,[1,5]) repmat(0,[1,9])]
   % yout would be
   % yout = [0.5000; 0.4983; 0.4902; 0.4749; 0.4523; 0.4224; 0.3854; 0.3420; 0.2944; 0.2450; 0.1964;
   %         0.1510; 0.1106; 0.0760; 0.0473; 0.0244; 0.0074;-0.0038;-0.0091;-0.0086;-0.0022];

   % uout = 1.0e-004 * [-0.1160   -0.0992   -0.0931   -1.0000    0.9172   -0.0543    0.0843,
   %                     0.0948    0.0828    0.1712    0.0647    0.0497    0.0508    0.8437,
   %                    -0.0500   -0.0807   -0.0440    0.0948   -0.0215   -0.0160]
   % ObjVal = 5.8739
   % yout would be
   % yout = [0.5000; 0.4997; 0.4979; 0.4925; 0.4776; 0.4507; 0.4164; 0.3766; 0.3323; 0.2849; 0.2360;
   %         0.1873; 0.1401; 0.0955; 0.0564; 0.0288; 0.0164; 0.0186; 0.0349; 0.0655; 0.1108];

   


   % results from LQG and PP desgn (see autolqg)
   chromlqgd1 = [2.9219 -5.2754 2.4038 0.3625 0.0754];  % weights: 10 and 2.5e3
   chromlqgd2 = [0.5467 -1.0674 0.5213 0.0810 0.0164];  % weights:  1 and 2.5e6
   chromppd1 = [6.0105 -11.6612 5.6564 0.8766 0.1775] 

   % Genetic Optimization of:  objauto1    Date: 10-Mar-95   Time: 14:57:01
   % Additional parameters:    P1 = 3  P2 = 20  P3 = 1, mit au_dsim4 (white noise)
   %    0.341538   0.0181329  -0.0177882   -0.467955  -0.0456201


   % Genetic Optimization of:  objauto1    Date: 15-Mar-95   Time: 10:25:47
   % Additional parameters:    P1 = 3  P2 = 20  P3 = 1, critical root: 0.95, noise: 0.001
   %    0.377116   0.0438137  -0.0422879    -0.78378    0.235023
   % continuous: 0.7250  0.8622  0.3112  14.4807  92.0400


   % Genetic Optimization of:  objauto1    Date: 13-Mar-95   Time: 10:28:25
   % Additional parameters:    P1 = 1  P2 = 20  P3 = 1, mit noise (au_csim3)
   % 0.232932  0.00174229 9.99928e-06     119.755     115.678
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