【PID优化】基于蝗虫算法PID控制器优化设计含Matlab源码

1 内容介绍

该文针对广泛应用的PID控制器,在MATLAB仿真软件环境下,开发出一个过程控制系统的仿真软件包,能够实现模型辨识和PID参数调节,为过程控制系统仿真研究提供了方便. 该软件界面友好,操作简便,具有实际应用价值.​

2 仿真代码

%_________________________________________________________________________%

%  Grasshopper Optimization Algorithm (GOA) source codes demo V1.0        %

%                                                                         %

%  Developed in MATLAB R2014a                                             %

% The Grasshopper Optimization Algorithm

function [TargetFitness,TargetPosition,Convergence_curve,Trajectories,fitness_history, position_history]=GOA(N, Max_iter, lb,ub, dim, fobj)

disp('GOA is now estimating the global optimum for your problem....')

flag=0;

if size(ub,1)==1

    ub=ones(dim,1)*ub;

    lb=ones(dim,1)*lb;

end

if (rem(dim,2)~=0) % this algorithm should be run with a even number of variables. This line is to handle odd number of variables

    dim = dim+1;

    ub = [ub; 100];

    lb = [lb; -100];

    flag=1;

end

%Initialize the population of grasshoppers

GrassHopperPositions=initialization(N,dim,ub,lb);

GrassHopperFitness = zeros(1,N);

fitness_history=zeros(N,Max_iter);

position_history=zeros(N,Max_iter,dim);

Convergence_curve=zeros(1,Max_iter);

Trajectories=zeros(N,Max_iter);

cMax=1;

cMin=0.00004;

%Calculate the fitness of initial grasshoppers

for i=1:size(GrassHopperPositions,1)

    if flag == 1

        GrassHopperFitness(1,i)=fobj(GrassHopperPositions(i,1:end-1));

    else

        GrassHopperFitness(1,i)=fobj(GrassHopperPositions(i,:));

    end

    fitness_history(i,1)=GrassHopperFitness(1,i);

    position_history(i,1,:)=GrassHopperPositions(i,:);

    Trajectories(:,1)=GrassHopperPositions(:,1);

end

[sorted_fitness,sorted_indexes]=sort(GrassHopperFitness);

% Find the best grasshopper (target) in the first population 

for newindex=1:N

    Sorted_grasshopper(newindex,:)=GrassHopperPositions(sorted_indexes(newindex),:);

end

TargetPosition=Sorted_grasshopper(1,:);

TargetFitness=sorted_fitness(1);

% Main loop

l=2; % Start from the second iteration since the first iteration was dedicated to calculating the fitness of antlions

while l

    

    c=cMax-l*((cMax-cMin)/Max_iter); % Eq. (2.8) in the paper

    

    for i=1:size(GrassHopperPositions,1)

        temp= GrassHopperPositions';

        for k=1:2:dim

            S_i=zeros(2,1);

            for j=1:N

                if i~=j

                    Dist=distance(temp(k:k+1,j), temp(k:k+1,i)); % Calculate the distance between two grasshoppers

                    

                    r_ij_vec=(temp(k:k+1,j)-temp(k:k+1,i))/(Dist+eps); % xj-xi/dij in Eq. (2.7)

                    xj_xi=2+rem(Dist,2); % |xjd - xid| in Eq. (2.7) 

                    

                    s_ij=((ub(k:k+1) - lb(k:k+1))*c/2)*S_func(xj_xi).*r_ij_vec; % The first part inside the big bracket in Eq. (2.7)

                    S_i=S_i+s_ij;

                end

            end

            S_i_total(k:k+1, :) = S_i;

            

        end

        

        X_new = c * S_i_total'+ (TargetPosition); % Eq. (2.7) in the paper      

        GrassHopperPositions_temp(i,:)=X_new'; 

    end

    % GrassHopperPositions

    GrassHopperPositions=GrassHopperPositions_temp;

    

    for i=1:size(GrassHopperPositions,1)

        % Relocate grasshoppers that go outside the search space 

        Tp=GrassHopperPositions(i,:)>ub';Tm=GrassHopperPositions(i,:)

        

        % Calculating the objective values for all grasshoppers

        if flag == 1

            GrassHopperFitness(1,i)=fobj(GrassHopperPositions(i,1:end-1));

        else

            GrassHopperFitness(1,i)=fobj(GrassHopperPositions(i,:));

        end

        fitness_history(i,l)=GrassHopperFitness(1,i);

        position_history(i,l,:)=GrassHopperPositions(i,:);

        

        Trajectories(:,l)=GrassHopperPositions(:,1);

        

        % Update the target

        if GrassHopperFitness(1,i)

            TargetPosition=GrassHopperPositions(i,:);

            TargetFitness=GrassHopperFitness(1,i);

        end

    end

        

    Convergence_curve(l)=TargetFitness;

    disp(['In iteration #', num2str(l), ' , target''s objective = ', num2str(TargetFitness)])

    

    l = l + 1;

end

if (flag==1)

    TargetPosition = TargetPosition(1:dim-1);

end

3 运行结果

4 参考文献

[1]周蓓晨, 张宏立. 基于SOA-MC算法的二自由度PID控制器优化设计[J]. 计算机仿真, 2018, 35(4):5.

[1]彭珍瑞, 栾睿, 王娴. 基于人工鱼群算法的伺服系统PID控制器参数优化[J]. 兰州交通大学学报, 2012, 31(4):4.

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