蚁群算法ACO的PID参数整定
蚁群算法ACO的PID参数整定：代码参考链接：链接：https://pan.baidu.com/s/15nqkVj9-WuwI-2HQ1gB7pg 提取码：howq clc,clear,close all
warning off
feature jit off
load('U.mat')
load('Y.mat')
% 对Y进行抽样
Y1 = Y(1:1000:end);
% ACO 参数
maxiter = 100;% 迭代次数
sizepop = 10;% 种群数量
popmin = [-1,-1,-1, 0.1, 0.1, 0.1 ]; % x_min
popmax = ; % x_max
Rou = 0.8; % 信息素增量强度
P0 = 0.1; % 转移概率
nvar = 6; % nvar个自变量
% 初始化种群
for i=1:sizepop
pop(i,:) = popmin + (popmax-popmin).*rand(1,nvar)*0.5;
fitness(i) = BJ_Fun2( pop(i,:), Y1 );
end
% 记录一组最优值
=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
for i=1:maxiter
lamda = 1/i; % 信息素挥发因子
=min(fitness);
for j=1:sizepop
Pt(j) = (fitness(j)-bestfit)./bestfit;
end
for j=1:sizepop
% 转移概率值
if Pt(j)<P0
pop(j,:) = pop(j,:) + (2*rand(1, nvar)-1)*lamda/2;
else
pop(j,:) = pop(j,:) + (popmax-popmin).*(rand(1, nvar)-0.5);
end
% 越界处理
for k=1:nvar
if pop(j,k)>popmax(k)
pop(j,k)=popmax(k);
end
if pop(j,k)<popmin(k)
pop(j,k)=popmin(k);
end
end
% 适应度值
fitness1 = BJ_Fun2(pop(j,:), Y1);
fitness2 = BJ_Fun2(gbest(j,:), Y1);
if fitness1<fitness2
gbest(j,:) = pop(j,:);
else
pop(j,:) = gbest(j,:);
end
fitness(j) = (1-Rou)*fitness(j) + BJ_Fun2(pop(j,:), Y1);
if fitness(j) < fitnesszbest
fitnesszbest = fitness(j);
fitnessgbest(j) = BJ_Fun2(pop(j,:), Y1);
end
end
= min(fitnessgbest);
end
zbest = pop(index,:);% 最优个体
save zbest.mat zbest
%% 显示
disp('最优解')
disp(['最优解： ', num2str( zbest )])
fprintf('\n')
disp(['最优解对应的目标函数值： ', num2str( bestfitness )])
disp('传递函数');
sys = tf( zbest(4), )
%% 画图显示
figure('color',)
plot(fitness_iter,'ro-','linewidth',2)
grid on
xlabel('迭代次数');
ylabel('适应度函数值')
%% 响应曲线
Ygap = 100;
= BJ_Fun_response2(zbest, Y(1:Ygap:end));
figure;hold on;
plot(rint_BFO.*max(Y(1:Ygap:end)),'r-','linewidth',2);
plot(yout_BFO.*max(Y(1:Ygap:end)),'b.-','linewidth',2);
legend('阶跃控制信号','ACO算法优化的信号逼近')
axis tight;
hold off适应度函数如下：
function fitness = BJ_Fun2(xx, Y1)
ts=0.001;
KP=xx(1); KI=xx(2); KD=xx(3);
aa=xx(4); bb=xx(5); cc=xx(6);
% sys = tf( , );
sys = tf( aa, );
dsys = c2d(sys, ts, 'z');
= tfdata( dsys, 'v' );
u_1 = 0; u_2=0;
y_1 = 0; y_2=0;
error_1 = 0;
x = ';
for k=1:length(Y1)
timef(k) = k*ts;
r(k) = Y1(k)./(max(Y1)+eps);
u(k) = KP*x(1)+KI*x(2)+KD*x(3);
yout(k) = -den(2)*y_1 -den(3)*y_2 + num(2)*u_1 + num(3)*u_2;
error(k) = r(k) - yout(k);
% PID参数返回值
u_2 = u_1;
u_1 = u(k);
y_2 = y_1;
y_1 = yout(k);
x(1) = error(k); % Kp
x(2) = x(2) + error(k)*ts; % KI
x(3) = (error(k)-error_1)/ts; % KD
error_1 = error(k);
end
fitness = sum( abs(error) ) + 300*min( abs( error ) );响应度曲线：
function = BJ_Fun_response2(xx, Y1)
KP=xx(1); KI=xx(2); KD=xx(3);
aa=xx(4); bb=xx(5); cc=xx(6);
ts=0.001;
% sys = tf( , );
sys = tf( aa, );
dsys = c2d(sys, ts, 'z');
= tfdata( dsys, 'v' );
u_1 = 0; u_2=0;
y_1 = 0; y_2=0;
error_1 = 0;
x = ';
for k=1:length(Y1)
timef(k) = k*ts;
r(k) = Y1(k)./(max(Y1)+eps);
u(k) = KP*x(1)+KI*x(2)+KD*x(3);
yout(k) = -den(2)*y_1 -den(3)*y_2 + num(2)*u_1 + num(3)*u_2;
error(k) = r(k) - yout(k);
% PID参数返回值
u_2 = u_1;
u_1 = u(k);
y_2 = y_1;
y_1 = yout(k);
x(1) = error(k); % Kp
x(2) = x(2) + error(k)*ts; % KI
x(3) = (error(k)-error_1)/ts; % KD
error_1 = error(k);
end
% fitness = sum( abs(error) ) + 300*min( abs( error ) );
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