PID神经元网络解耦控制算法讲解Word格式.docx
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PID神经元网络解耦控制算法讲解Word格式.docx
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V(i,:
)=0.003*rands(1,45);
%初始化速度
end
%%初始种群极值
%找最好的染色体
[bestfitnessbestindex]=min(fitness);
zbest=pop(bestindex,:
);
%全局最佳
gbest=pop;
%个体最佳
fitnessgbest=fitness;
%个体最佳适应度值
fitnesszbest=bestfitness;
%全局最佳适应度值
%%迭代寻优
maxgen
i
forj=1:
sizepop
w=(wmax-wmin)*(i-1)/(maxgen)+wmin;
%权值线性变化
V(j,:
)=w*V(j,:
)+c1*rand*(gbest(j,:
)-pop(j,:
))+c2*rand*(zbest-pop(j,:
%速度更新
V(j,find(V(j,:
)>
Vmax))=Vmax;
%小于最大速度
)<
Vmin))=Vmin;
%大于最小速度
%种群更新
pop(j,:
)=pop(j,:
)+0.5*V(j,:
fork=1:
45
ifrand>
0.95
pop(j,k)=0.3*rand;
%自适应变异
end
pop(j,find(pop(j,:
popmax))=popmax;
%小于个体最大值
popmin))=popmin;
%大于个体最小值
%适应度值
fitness(j)=fun(pop(j,:
%个体极值更新
iffitness(j)<
fitnessgbest(j)
gbest(j,:
)=pop(j,:
fitnessgbest(j)=fitness(j);
%全局极值更新
fitnesszbest
zbest=pop(j,:
fitnesszbest=fitness(j);
%记录最优适应度值
yy(i)=fitnesszbest;
%%最优个体控制
figure
(1)
plot(yy)
title('
粒子群算法进化过程'
xlabel('
进化代数'
ylabel('
适应度'
individual=zbest;
w11=reshape(individual(1:
6),3,2);
w12=reshape(individual(7:
12),3,2);
w13=reshape(individual(13:
18),3,2);
w21=individual(19:
27);
w22=individual(28:
36);
w23=individual(37:
45);
rate1=0.006;
rate2=0.001;
%学习率
k=0.3;
K=3;
y_1=zeros(3,1);
y_2=y_1;
y_3=y_2;
%输出值
u_1=zeros(3,1);
u_2=u_1;
u_3=u_2;
%控制率
h1i=zeros(3,1);
h1i_1=h1i;
%第一个控制量
h2i=zeros(3,1);
h2i_1=h2i;
%第二个控制量
h3i=zeros(3,1);
h3i_1=h3i;
%第三个空置量
x1i=zeros(3,1);
x2i=x1i;
x3i=x2i;
x1i_1=x1i;
x2i_1=x2i;
x3i_1=x3i;
%隐含层输出
%权值初始化
k0=0.03;
%值限定
ynmax=1;
ynmin=-1;
%系统输出值限定
xpmax=1;
xpmin=-1;
%P节点输出限定
qimax=1;
qimin=-1;
%I节点输出限定
qdmax=1;
qdmin=-1;
%D节点输出限定
uhmax=1;
uhmin=-1;
%输出结果限定
fork=1:
1:
200
%--------------------------------网络前向计算--------------------------
%系统输出
y1(k)=(0.4*y_1
(1)+u_1
(1)/(1+u_1
(1)^2)+0.2*u_1
(1)^3+0.5*u_1
(2))+0.3*y_1
(2);
y2(k)=(0.2*y_1
(2)+u_1
(2)/(1+u_1
(2)^2)+0.4*u_1
(2)^3+0.2*u_1
(1))+0.3*y_1(3);
y3(k)=(0.3*y_1(3)+u_1(3)/(1+u_1(3)^2)+0.4*u_1(3)^3+0.4*u_1
(2))+0.3*y_1
(1);
r1(k)=0.7;
r2(k)=0.4;
r3(k)=0.6;
%控制目标
%系统输出限制
yn=[y1(k),y2(k),y3(k)];
yn(find(yn>
ynmax))=ynmax;
yn(find(yn<
ynmin))=ynmin;
%输入层输出
x1o=[r1(k);
yn
(1)];
x2o=[r2(k);
yn
(2)];
x3o=[r3(k);
yn(3)];
%隐含层
x1i=w11*x1o;
x2i=w12*x2o;
x3i=w13*x3o;
%比例神经元P计算
xp=[x1i
(1),x2i
(1),x3i
(1)];
xp(find(xp>
xpmax))=xpmax;
xp(find(xp<
xpmin))=xpmin;
qp=xp;
h1i
(1)=qp
(1);
h2i
(1)=qp
(2);
h3i
(1)=qp(3);
%积分神经元I计算
xi=[x1i
(2),x2i
(2),x3i
(2)];
qi=[0,0,0];
qi_1=[h1i
(2),h2i
(2),h3i
(2)];
qi=qi_1+xi;
qi(find(qi>
qimax))=qimax;
qi(find(qi<
qimin))=qimin;
h1i
(2)=qi
(1);
h2i
(2)=qi
(2);
h3i
(2)=qi(3);
%微分神经元D计算
xd=[x1i(3),x2i(3),x3i(3)];
qd=[000];
xd_1=[x1i_1(3),x2i_1(3),x3i_1(3)];
qd=xd-xd_1;
qd(find(qd>
qdmax))=qdmax;
qd(find(qd<
qdmin))=qdmin;
h1i(3)=qd
(1);
h2i(3)=qd
(2);
h3i(3)=qd(3);
%输出层计算
wo=[w21;
w22;
w23];
qo=[h1i'
h2i'
h3i'
];
qo=qo'
;
uh=wo*qo;
uh(find(uh>
uhmax))=uhmax;
uh(find(uh<
uhmin))=uhmin;
u1(k)=uh
(1);
u2(k)=uh
(2);
u3(k)=uh(3);
%控制律
%--------------------------------------网络反馈修正----------------------
%计算误差
error=[r1(k)-y1(k);
r2(k)-y2(k);
r3(k)-y3(k)];
error1(k)=error
(1);
error2(k)=error
(2);
error3(k)=error(3);
J(k)=0.5*(error
(1)^2+error
(2)^2+error(3)^2);
%调整大小
ypc=[y1(k)-y_1
(1);
y2(k)-y_1
(2);
y3(k)-y_1(3)];
uhc=[u_1
(1)-u_2
(1);
u_1
(2)-u_2
(2);
u_1(3)-u_2(3)];
%隐含层和输出层权值调整
%调整w21
Sig1=sign(ypc./(uhc
(1)+0.00001));
dw21=sum(error.*Sig1)*qo'
w21=w21+rate2*dw21;
%调整w22
Sig2=sign(ypc./(uh
(2)+0.00001));
dw22=sum(error.*Sig2)*qo'
w22=w22+rate2*dw22;
%调整w23
Sig3=sign(ypc./(uh(3)+0.00001));
dw23=sum(error.*Sig3)*qo'
w23=w23+rate2*dw23;
%输入层和隐含层权值调整
delta2=zeros(3,3);
wshi=[w21;
fort=1:
3
delta2(1:
3,t)=error(1:
3).*sign(ypc(1:
3)./(uhc(t)+0.00000001));
sgn(j)=sign((h1i(j)-h1i_1(j))/(x1i(j)-x1i_1(j)+0.00001));
s1=sgn'
*[r1(k),y1(k)];
wshi2_1=wshi(1:
3,1:
3);
alter=zeros(3,1);
dws1=zeros(3,2);
forp=1:
alter(j)=alter(j)+delta2(p,:
)*wshi2_1(:
j);
dws1(p,:
)=alter(p)*s1(p,:
w11=w11+rate1*dws1;
%调整w12
sgn(j)=sign((h2i(j)-h2i_1(j))/(x2i(j)-x2i_1(j)+0.0000001));
s2=sgn'
*[r2(k),y2(k)];
wshi2_2=wshi(:
4:
6);
alter2=zeros(3,1);
dws2=zeros(3,2);
alter2(j)=alter2(j)+delta2(p,:
)*wshi2_2(:
dws2(p,:
)=alter2(p)*s2(p,:
w12=w12+rate1*dws2;
%调整w13
sgn(j)=sign((h3i(j)-h3i_1(j))/(x3i(j)-x3i_1(j)+0.0000001));
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- 关 键 词:
- PID 神经元 网络 控制 算法 讲解