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Results = augood1(what)
different best results from optimizations discrete simulation:
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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