lunwen.m

matlab进行模糊pid仿真的源代码

%模糊PID控制与常规PID控制的仿真对比
clear all;
close all;

%常规PID控制
ts=1;td=15;Nd=td/ts;
sys=tf(4,[720,1]);
dsys=c2d(sys,ts,'tustin');
[num,den]=tfdata(dsys,'v');

%给出各变量的初始参数
kp0=2.64;
kd0=198;
ki0=0.0088;

u_11=0.0;u_21=0.0;u_31=0.0;

y_11=0;y_21=0;y_31=0;

x1=[0,0,0]';

error_11=0;
e_11=0.0;
ec_11=0.0;

%设计循环环节
for k=1:1:5000 
time(k)=k*ts;

rin(k)=800;%给出控制的目标值

u1(k)=kp0*x1(1)+kd0*x1(2)+ki0*x1(3);%计算PID控制器的输出
if k<=Nd % 设计延迟环节
    u1(k)=0;
else
   yout1(k)=y_11;
end
if u1(k)>=1000%设计限幅环节
u1(k)=1000;
end
if u1(k)<=-1000
u1(k)=-1000;
end

yout1(k)=-den(2)*y_11+num(2)*u_11;%线性模型
error1(k)=rin(k)-yout1(k);%计算误差
%Return of PID parameters
u_31=u_21;u_21=u_11;u_11=u1(k);

y_31=y_21;y_21=y_11;y_11=yout1(k);

x1(1)=error1(k); % Calculating P
x1(2)=error1(k)-error_11; % Calculating D
x1(3)=x1(3)+error1(k); % Calculating I
e_11=x1(1);
ec_11=x1(2);

error_11=error1(k);
end
%模糊PID控制
a=newfis('fuzzpid');%建立模糊推理系统

a=addvar(a,'input','e',[-6,6]); %Parameter e
a=addmf(a,'input',1,'NB','trimf',[-6,-6,-4]);
a=addmf(a,'input',1,'NM','trimf',[-6,-4,-2]);
a=addmf(a,'input',1,'NS','trimf',[-4,-2,0]);
a=addmf(a,'input',1,'Z','trimf', [-2,0,2]);
a=addmf(a,'input',1,'PS','trimf',[0,2,4]);
a=addmf(a,'input',1,'PM','trimf',[2,4,6]);
a=addmf(a,'input',1,'PB','trimf',[4,6,6]);

a=addvar(a,'input','ec',[-6,6]); %Parameter ec
a=addmf(a,'input',2,'NB','trimf',[-6,-6,-4]);
a=addmf(a,'input',2,'NM','trimf',[-6,-4,-2]);
a=addmf(a,'input',2,'NS','trimf',[-4,-2,0]);
a=addmf(a,'input',2,'Z','trimf', [-2,0,2]);
a=addmf(a,'input',2,'PS','trimf',[0,2,4]);
a=addmf(a,'input',2,'PM','trimf',[2,4,6]);
a=addmf(a,'input',2,'PB','trimf',[4,6,6]);

a=addvar(a,'output','kp',[-6,6]); %Parameter kp
a=addmf(a,'output',1,'NB','trimf',[-6,-6,-4]);
a=addmf(a,'output',1,'NM','trimf',[-6,-4,-2]);
a=addmf(a,'output',1,'NS','trimf',[-4,-2,0]);
a=addmf(a,'output',1,'Z','trimf', [-2,0,2]);
a=addmf(a,'output',1,'PS','trimf',[0,2,4]);
a=addmf(a,'output',1,'PM','trimf',[2,4,6]);
a=addmf(a,'output',1,'PB','trimf',[4,6,6]);


a=addvar(a,'output','ki',[-6,6]); %Parameter ki
a=addmf(a,'output',2,'NB','trimf',[-6,-6,-4]);
a=addmf(a,'output',2,'NM','trimf',[-6,-4,-2]);
a=addmf(a,'output',2,'NS','trimf',[-4,-2,0]);
a=addmf(a,'output',2,'Z','trimf', [-2,0,2]);
a=addmf(a,'output',2,'PS','trimf',[0,2,4]);
a=addmf(a,'output',2,'PM','trimf',[2,4,6]);
a=addmf(a,'output',2,'PB','trimf',[4,6,6]);

a=addvar(a,'output','kd',[-6,6]); %Parameter kd
a=addmf(a,'output',3,'NB','trimf',[-6,-6,-4]);
a=addmf(a,'output',3,'NM','trimf',[-6,-4,-2]);
a=addmf(a,'output',3,'NS','trimf',[-4,-2,0]);
a=addmf(a,'output',3,'Z','trimf', [-2,0,2]);
a=addmf(a,'output',3,'PS','trimf',[0,2,4]);
a=addmf(a,'output',3,'PM','trimf',[2,4,6]);
a=addmf(a,'output',3,'PB','trimf',[4,6,6]);

rulelist=[1 1 7 1 5 1 1;%规则矩阵
1 2 7 1 3 1 1;
1 3 6 2 1 1 1;
1 4 6 2 1 1 1;
1 5 5 3 1 1 1;
1 6 4 4 2 1 1;
1 7 4 4 5 1 1;

2 1 7 1 5 1 1;
2 2 7 1 3 1 1;
2 3 6 2 1 1 1;
2 4 5 3 2 1 1;
2 5 5 3 2 1 1;
2 6 4 4 3 1 1;
2 7 3 4 4 1 1;

3 1 6 1 4 1 1;
3 2 6 2 3 1 1;
3 3 6 3 2 1 1;
3 4 5 3 2 1 1;
3 5 4 4 3 1 1;
3 6 3 5 3 1 1;
3 7 3 5 4 1 1;

4 1 6 2 4 1 1;
4 2 6 2 3 1 1;
4 3 5 3 3 1 1;
4 4 4 4 3 1 1;
4 5 3 5 3 1 1;
4 6 2 6 3 1 1;
4 7 2 6 4 1 1;

5 1 5 2 4 1 1;
5 2 5 3 4 1 1;
5 3 4 4 4 1 1;
5 4 3 5 4 1 1;
5 5 3 5 4 1 1;
5 6 2 6 4 1 1;
5 7 2 7 4 1 1;

6 1 5 4 7 1 1;
6 2 4 4 5 1 1;
6 3 3 5 5 1 1;
6 4 2 5 5 1 1;
6 5 2 6 5 1 1;
6 6 2 7 5 1 1; 
6 7 1 7 7 1 1;

7 1 4 4 7 1 1; 
7 2 4 4 6 1 1;
7 3 2 5 6 1 1;
7 4 2 6 6 1 1;
7 5 2 6 5 1 1;
7 6 1 7 5 1 1;
7 7 1 7 7 1 1];

a=addrule(a,rulelist);%加入模糊规则推理表
a=setfis(a,'DefuzzMethod','centroid');%设计去模糊的方法
writefis(a,'fuzzpid');

a=readfis('fuzzpid');% 装入FIS

%PID Controller
u_1=0.0;u_2=0.0;u_3=0.0;%给各参数赋初值
y_1=0;y_2=0;y_3=0;
x=[0,0,0]';
error_1=0;
e_1=0.0;
ec_1=0.0;
dkp=0;
dki=0;
dkd=0;
x1=0;
x2=0;
for k=1:1:5000
time(k)=k*ts;
ke=0.0075;%给出比例因子和量化因子
kec=0.015;
kup=1/50;
kui=1/300;
kud=1/6;
rin(k)=800;
%Using fuzzy inference to fuzzy PID
k_pid=evalfis([e_1,ec_1],a);% 计算模糊控制器的输出
dkp=kup*k_pid(1);
dki=kui*k_pid(2);
dkd=kud*k_pid(3); 
kp(k)=kp0+dkp;%计算当前的PID参数
ki(k)=ki0+dki;
kd(k)=kd0+dkd;
u(k)=kp(k)*x(1)+kd(k)*x(2)+ki(k)*x(3);
if k<=Nd %设计延迟环节
    u(k)=0;
else
   yout(k)=y_1;
end
if u(k)>=1000%设计限幅环节
u(k)=1000;
end
if u(k)<=-1000
u(k)=-1000;
end

yout(k)=-den(2)*y_1+num(2)*u_1;
error(k)=rin(k)-yout(k);
%%%%%%%%%%%%%%Return of PID parameters%%%%%%%%%%%%%%%
u_3=u_2;u_2=u_1;u_1=u(k);

y_3=y_2;y_2=y_1;y_1=yout(k);

x(1)=error(k); % Calculating P
x(2)=error(k)-error_1; % Calculating D
x(3)=x(3)+error(k); % Calculating I
x1=ke*x(1);%将实际误差模糊化
x2=kec*x(2); %将实际误差的变化率模糊化
e_1=x1;
ec_1=x2;

error_1=error(k);
end
showrule(a);
figure(1);plot(time,rin,'b',time,yout,'r',time,yout1,'k');%绘制响应曲线
xlabel('time(s)');ylabel('rin,yout,yout1');
gtext('模糊PID控制');gtext('PID控制');
figure(2);plot(time,error,'r');
xlabel('time(s)');ylabel('error');
figure(4);plot(time,kp,'r');
xlabel('time(s)');ylabel('kp');
figure(5);plot(time,ki,'r');
xlabel('time(s)');ylabel('ki');
figure(6);plot(time,kd,'r');
xlabel('time(s)');ylabel('kd');
plotfis(a);
fuzzy fuzzpid.fis