Error in user-supplied fitness function evaluation - Genetic Algorithm (2024)

function J = simplanta(individuo)

Np = individuo(1);

Nc = individuo(2);

rw = individuo(3);

ts=1/2000; % sampling time

t=0:ts:1; % sim time

f0=50;

r=1+1.5.*sin(2*pi*f0*t); % reference signal

Ldata=length(t); % # of time points

%Discrete Model

Ad =[0.4134 0.4536 0; -0.2404 0.7876 0; -0.4366 0.4224 0.7738];

Bd =[0.1331; 0.4528; 0.1273];

Cd=[0 0 1];

% Specified Gain Calc

n=length(Ad); %order%

Omega = 2*pi*f0*ts;

gamma = ( 2*cos(Omega) ) + 1;

E=[0 1 0; 0 0 1; 1 -gamma gamma];

sigma = [0;0;1];

% prediction matrices

A=[Ad zeros(n,3); sigma*Cd*Ad E];

B=[Bd; sigma*Cd*Bd];

C=[zeros(1,3) sigma' ];

% MPC

nau = length(A);

Rb = rw*eye(Nc,Nc);

% F e Phi

F=zeros(Np,nau);

for i=1:Np

F(i,:)=C*(A^i);

end

Phi = zeros(Np, Nc);

%1st column:

for linha=1:Np

Phi(linha,1) = C*(A^(linha-1))*B;

end

%other column

for col =2: Nc

Phi(col:Np, col) = Phi(1:Np-(col-1),1);

end

PTP = Phi'*Phi;

if (Nc == 1)

vetor10 = [1];

else

vetor10 = [ 1 zeros(1,Nc-1)];

end

Hessiana=inv(PTP + Rb);

Kbs = -1*vetor10*Hessiana*(Phi')*F;

% control sim

x = zeros(n,Ldata+1); %

y = zeros(1,Ldata); %

erro =zeros(1,Ldata); %

u =zeros(1,Ldata); %

xk_1=zeros(n,1); %x(k-1)

xk_2=zeros(n,1); %x(k-2)

xk_3=zeros(n,1); %x(k-3)

uk_1=0; %u(k-1)

uk_2=0; %u(k-2)

uk_3=0; %u(k-3)

ek_1=0; %e(k-1)

ek_2=0; %e(k-2)

% closed loop sim

for k=1:Ldata

xk = x(:,k); %

rk = r(k);

yk = Cd*xk; %

ek = rk-yk; %

qk = xk_3+(-gamma*xk_2)+(gamma*xk_1)-xk;

xbarra= [qk;ek_2;ek_1;ek];

vk=Kbs*xbarra;

uk= uk_3 + gamma*(uk_1-uk_2)-vk;

%

x(:,k+1) = (Ad*xk)+(Bd*uk);

%

erro(k) = ek;

u(k) = uk;

y(k) = yk;

%

xk_3 = xk_2;

xk_2 = xk_1;

xk_1 = xk;

uk_3 = uk_2;

uk_2 = uk_1;

uk_1 = uk;

ek_2 = ek_1;

ek_1 = ek;

end

%Fit func

up = max(abs(u))/1000; %peak control law

erro_rmse = rmse(r,y); % RMSE

% settling time

max_e = max(erro); %

valor_acom = 0.02*max_e; %

ind_acom = find(erro >= -valor_acom & erro <= valor_acom, 1); %

t_ae = t(ind_acom); %

J=0.2*up + 0.7*erro_rmse + 0.1*t_ae;

A_des = [0 0 -1; -1 1 0; 1 0 0; 0 -1 0];

b_des = [0 0 100 -2]';

intcon = [1 2]; os

lb = [2 2 0];

ub = [100 99 60];

%Declaração das opções de funcionamento do AG

options = optimoptions("ga","CreationFcn","gacreationuniformint",...

"Display","iter","MutationFcn","mutationpower",...

"SelectionFcn","selectionroulette",...

"PlotFcn",["gaplotdistance", "gaplotselection","gaplotstopping",...

"gaplotbestf","gaplotbestindiv"]);

[best_ind, cost] = ga(@simplanta, 3, A_des, b_des, [], [], lb, ub, [],...

intcon, options)

param = infoPlant(best_ind)

Unable to perform assignment because the size of the left side is 1-by-1 and the

size of the right side is 1-by-0.

Error in fcnvectorizer (line 19)

y(i,:) = feval(fun,(pop(i,:)));

Error in gaminlppenaltyfcn

Error in gapenalty

Error in stepGA (line 63)

nextScore = FitnessFcn(pop);

Error in galincon (line 86)

[score,population,state] = stepGA(score,population,options,state,GenomeLength,FitnessFcn);

Error in gapenalty

Error in ga (line 412)

[x,fval,exitFlag,output,population,scores] = gapenalty(FitnessFcn,nvars,...

Error in ga_implementation (line 60)

[best_ind, cost] = ga(@simplanta, 3, A_des, b_des, [], [], lb, ub, [],...

Caused by:

Failure in user-supplied fitness function evaluation. GA cannot continue.

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