基于帝国竞争优化算法ICA的matlab仿真+仿真录像

function f=testfunction11(x) % 20 % 22 MIMO PID Tunuing bias = 2; funnum = -1; alpha = 2; if funnum==-1 %F1 Rasrigin f = 18.3549 + 20 + x(:,1).^2 + x(:,2).^2 - 10 * ( cos(alpha*pi*x(:,1)) + cos(alpha*pi*x(:,2)) ); elseif funnum==0 %F1 f = (x(:,1) - bias).^2 + (x(:,2) - bias).^2; elseif funnum==1 %F2 f=abs(x)+cos(x); elseif funnum==2 %F2 f=abs(x)+sin(x); elseif funnum==3 %F3 f=x(:,1).^2+x(:,2).^2; elseif funnum==4 %F4 f=100*(x(:,2).^2-x(:,1)).^2+(1-x(:,1)).^2; elseif funnum==5 %F5 f(:,1)=sum(abs(x')-10*cos(sqrt(abs(10*x'))))'; elseif funnum==6 %F6 f=(x.^2+x).*cos(x); elseif funnum==7 %F7 % x = x * 10; f=x(:,1).*sin(4*x(:,1))+1.1*x(:,2).*sin(2*x(:,2)); elseif funnum==8 %F8 f=x(:,2).*sin(4*x(:,1))+1.1*x(:,1).*sin(2*x(:,2)); elseif funnum==9 %F9 f(:,1)=x(:,1).^4+2*x(:,2).^4+randn(length(x(:,1)),1); elseif funnum==10 %F10 f(:,1)=20+sum(x'.^2-10*cos(2*pi*x'))'; elseif funnum==11 %F11 f(:,1)=1+sum(abs(x').^2/4000)'-prod(cos(x'))'; elseif funnum==12 %F12 f(:,1)=.5+(sin(sqrt(x(:,1).^2+x(:,2).^2).^2)-.5)./(1+.1*(x(:,1).^2+x(:,2).^2)); elseif funnum==13 %F13 aa=x(:,1).^2+x(:,2).^2; bb=((x(:,1)+.5).^2+x(:,2).^2).^0.1; f(:,1)=aa.^0.25.*sin(30*bb).^2+abs(x(:,1))+abs(x(:,2)); elseif funnum==14 %F14 f(:,1)=besselj(0,x(:,1).^2+x(:,2).^2)+abs(1-x(:,1))/10+abs(1-x(:,2))/10; elseif funnum==15 %F15 f(:,1)=-exp(.2*sqrt((x(:,1)-1).^2+(x(:,2)-1).^2)+(cos(2*x(:,1))+sin(2*x(:,1)))); elseif funnum==16 %F16 f(:,1)=x(:,1).*sin(sqrt(abs(x(:,1)-(x(:,2)+9))))-(x(:,2)+9).*sin(sqrt(abs(x(:,2)+0.5*x(:,1)+9))); elseif funnum==17 %MOO function x=x+1; f(:,1)=(x(:,1)+x(:,2).^2+sqrt(x(:,3))+1./x(:,4))/8.5; f(:,2)=(1./x(:,1)+1./x(:,2)+x(:,3)+x(:,4))/6; elseif funnum == 18 % xN = (x-0.5)*2; % xN = (x); x = (x)*12; x1 = x(:,1); x2 = x(:,2); x1Min = 3; x2Min = 3; f = 20 + (x1 - x1Min).^2 + (x2 - x2Min).^2 - 10 * (cos(2*pi*(x1 - x1Min)) + cos(2*pi*(x2 - x2Min))); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% elseif funnum == 20 % Game Theory A = [20 20 20 25 25 25 30 30 30]; for i = 1 : size(x,1) f(i,1) = x(i,:)*A(:,3) - x(i,:)*A*x(i,:)'; end f=x(:,1).^2+x(:,2).^2; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% elseif funnum == 21 % PID Tuning s = tf('s'); % G = tf([1 2],[1 5 5 5]); G = 4.228/(s+0.5)/(s^2+1.64*s+8.456); for jj = 1:size(x,1) KP = x(jj,1); KI = x(jj,2); KD = x(jj,3); PID_Controller = KP + KD*s + KI/s; % G = (1 - 5*s) / ( (1+10*s)*(1+20*s) ) % % Arash System SERIES = series(G,PID_Controller); ClosedLoop = feedback(SERIES,1); gam = 0.01; t_in = [0:gam:40]; [y,t] = step(ClosedLoop,t_in); % step(ClosedLoop,t_in); % hold on % step(G); % [y,t] = step(G,t_in); %===================================================== % Finding Max Overshoot %===================================================== maxovershoot = 0; if max(y)>1 ind = find(y==max(y)); maxovershoot = (y(ind) - 1); % hold on % plot(t(ind),y(ind),'rp') % hold off end %===================================================== % Finding Rise Time %===================================================== y_1_10 = abs(y - 0.1); y_9_10 = abs(y - 0.9); index1_10 = find( y_1_10 < (5*min(y_1_10)) ); index1_10 = index1_10(1); index9_10 = find( y_9_10 < (5*min(y_9_10)) ); index9_10 = index9_10(1); RiseTime = t(index9_10)-t(index1_10);y_1_10 = abs(y - 0.1); % hold on % plot(t(index9_10),y(index9_10),'gp' , t(index1_10),y(index1_10),'gp') % hold off %===================================================== % Finding Settling Time %===================================================== y_1_2_100 = abs(y - 1.05); y_0_98_100 = abs(y - 0.95); y_1 = abs(y - 1); index1_2_100 = find( y_1_2_100 < (0.01 + min(y_1_2_100)) ); index0_98_100 = find( y_0_98_100 < (0.01 + min(y_0_98_100)) ); % hold on % plot(t(index1_2_100),y(index1_2_100),'k.' , t(index0_98_100),y(index0_98_100),'y.') % hold off settling_time = t(end); settling_time_index = numel(t); all_indexes = sort([index1_2_100 ; index0_98_100]); for ii = 1:numel(all_indexes) if ( sum( (y_1(all_indexes(ii):end)>0.05) ) ) == 0 settling_time_index = all_indexes(ii); settling_time = t(all_indexes(ii)); break end end % hold on % plot(settling_time,y(settling_time_index),'kp') % hold off %===================================================== % Finding IAE %===================================================== IAE = sum(abs(y-1))*gam; %===================================================== % Returning %===================================================== f(jj,1) = 1*(maxovershoot + RiseTime + IAE + settling_time); end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% MIMO PID Tuning %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% elseif funnum == 22 % MIMO PI Tuning % x = (x-.5) * 2; s = tf('s'); % PADE APPROXIMATION IS USED TO APROXIMATE EXP(-TS) % G11 = 12.8*((1-.5*s+.5*(-.5*s)^2)/(1+.5*s+.5*(.5*s)^2))/(16.7*s+1); % G12 = -18.9*((1-1.5*s+.5*(-1.5*s)^2)/(1+1.5*s+.5*(1.5*s)^2))/(21*s+1); % G21 = 6.6*((1-3.5*s+.5*(-3.5*s)^2)/(1+3.5*s+.5*(+3.5*s)^2))/(10.9*s+1); % G22 = -19.4*((1-1.5*s+.5*(-1.5*s)^2)/(1+1.5*s+.5*(1.5*s)^2))/(14.4*s+1); % ApproxOrder = 2; [n1,d1] = pade(1,ApproxOrder); [n3,d3] = pade(3,ApproxOrder); [n7,d7] = pade(7,ApproxOrder); G11 = 12.8*tf(n1,d1)/(16.7*s+1); G12 = -18.9*tf(n3,d3)/(21*s+1); G21 = 6.6*tf(n7,d7)/(10.9*s+1); G22 = -19.4*tf(n3,d3)/(14.4*s+1); G = [ G11 G12; G21 G22]; gam = .1; t_in = [0:gam:800]; KP11 = x(:,1); KI11 = x(:,2); KP12 = x(:,3); KI12 = x(:,4); KD12 = x(:,5); KP21 = x(:,6); KI21 = x(:,7); KD21 = x(:,8); KP22 = x(:,9); KI22 = x(:,10); for jj = 1:size(x,1) jj % x = [KP11 KI11 | KP12 KI12 KD12 | KP21 KI21 KD21 | KP22 KI22 ] % % KP11 = 0.184; KI11 = 0.184/3.92; KP12 = -0.0102; KI12 = -0.0102/0.445; KD12 = -0.0102*(-0.804);... % KP21 = -0.0674; KI21 = -0.0674/(-4.23); KD21 = -0.0674 * 0.796; KP22 = -0.0660; KI22 = -0.0660/4.25; jj = 1; %Auto Tuning Controller % % % % % KP11 = 0.9971; KI11 = 0.0031; KP22 = -.0141; KI22 = -0.0071; % Their proposed algorithm % % KP11 = 0.5511; KI11 = 0.0018; KP22 = -.0182; KI22 = -0.0067; % Traditional GA % KP11 = 0.3750; KI11 = 0.0452; KP22 = -.0750; KI22 = -0.0032; % BLT Method