马上注册,结交更多好友,享用更多功能,让你轻松玩转社区。
您需要 登录 才可以下载或查看,没有账号?我要加入
x
本帖最后由 圣徒 于 2011-1-22 10:21 编辑
>> setlmis([]);%初始化LMI
>> X=lmivar(1,[4 1]);
>> W=lmivar(2,[1 4]);
>> q=lmivar(1,[1 0]);
>> lmiterm([1 1 1 X],A,1,'s');%添加内因子
>> lmiterm([1 1 1 W],B2,1,'s');%添加内因子
>> lmiterm([1 1 2 0],B1);
>> lmiterm([1 1 3 -X],1,C1');
>> lmiterm([1 1 3 -W],1,D);
>> lmiterm([1 2 2 0],-1);
>> lmiterm([1 2 3 0],0);
>> lmiterm([1 3 3 0],-q);
>> lmiterm([-2 1 1 X],1,1);
>> lmisys=getlmis;
>> c=mat2dec(lmisys,zeros(4),[0 0 0 0],1)
c =
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
>> [copt,xopt]=mincx(lmisys,c)
Solver for linear objective minimization under LMI constraints
Iterations : Best objective value so far
1
2
3
4
5
6
7 -4.769352e+008
8 -9.482209e+008
9 -9.482209e+008
10 -9.772119e+008
11 -9.772119e+008
12 -9.825143e+008
13 -9.825143e+008
*** new lower bound: -1.083984e+009
14 -9.914010e+008
*** new lower bound: -1.034580e+009
15 -9.934978e+008
*** new lower bound: -1.021295e+009
16 -9.951168e+008
*** new lower bound: -1.013824e+009
17 -9.964304e+008
*** new lower bound: -1.009358e+009
18 -9.971183e+008
*** new lower bound: -1.006591e+009
Result: feasible solution of required accuracy
best objective value: -9.971183e+008
guaranteed relative accuracy: 9.50e-003
f-radius saturation: 99.981% of R = 1.00e+009
copt =
-9.9712e+008
xopt =
1.0e+008 *
0.0000
-0.0000
0.0000
-0.0000
0.0000
0.0000
0.0000
-0.0000
-0.0000
0.0006
0.3517
-0.0023
-0.0257
-0.6429
-9.9712参数如下
A =
1.0e+003 *
0 0 0.0010 -0.0010
0 0 0 0.0010
-0.0563 0 -0.0031 0.0031
0.4500 -5.0000 0.0250 -0.0253
B1 =
0
-1.0000
0
0.2500
B2 =
0
0
0.0031
-0.0250
C1 =
-56.2500 0 -3.1250 3.1250
D =
0.0031
|