基于粒子群优化的MPPT控制

function [sys,x0,str,ts] = BFOA_PSO_pwm(t,x,u,flag) % %u为输入变量，u(1)为电流，u(2)为电压 % persistent first; persistent stop; persistent i; persistent mg; persistent count; persistent Uin; %Initialize the parameters NP = 6; %细菌种群数量 Ped = 0.005; %迁移概率，按照(NU-NL)/(NP*40)计算 %CR = 0.5; %可以控制收敛速度 XL = -1; %修改为pwm基准源最大最小值，[-1,1] XU = 1; maxgen = 100; Nc = 4; Nre = 3; MaxStep = 0.1; %迁移的最大速度 persistent w ; persistent Gbest; %persistent Gmaxval; persistent D_out; %输出给sys persistent D_out_current; %persistent U; %不再使用 %persistent V; persistent fitval_current; persistent fitval_new; persistent stable_flag; %电路进入稳定状态标记 persistent stable_count; %电路未稳定时，进行计数 persistent fit_order; persistent Nc_count; persistent Nre_count; persistent MoveStep; persistent MoveStep_PSO; persistent Pbest; %persistent Pbestval; persistent best_index; persistent c1; persistent c2; persistent r1; persistent r2; switch flag, case 0, sizes = simsizes; sizes.NumContStates = 0; sizes.NumDiscStates = 0; sizes.NumOutputs = 1; sizes.NumInputs = 2; sizes.DirFeedthrough = 0; sizes.NumSampleTimes = 1; sys = simsizes(sizes); x0=[]; str=[]; ts=[0.004 ,0.001]; % initialize the static variables first = 1; stop = 0; i = 0; mg = 0; count = 3; Nc_count = 0; Nre_count = 0; D_out = zeros(1, NP); %初始化D_out、U、fitval_current、fitval_new为全0 D_out_current = zeros(1,NP); best_index = 1; %U = zeros(1, NP); %V = zeros(1, NP); fitval_current = zeros(1, NP); fitval_new = zeros(1, NP); fit_order = zeros(1, NP); Pbest = zeros(1, NP); Gbest = 0; %Gmaxval = 0; %X(:) = unifrnd(XL, XU, 1,NP) D_out_current(:) = linspace(XL+0.005,XU-0.005,NP); %在[-1,1]间均匀取值 D_out(:) = D_out_current(:); MoveStep = unifrnd(-MaxStep, MaxStep, 1,NP); %初始化单个细菌的移动速度 MoveStep_PSO = zeros(1, NP); stable_flag = 1; %初始化，电路未稳定 stable_count = 0; c1 = 0.030; c2 = 0.030; r1 = rand(); r2 = rand(); case 3, %if count == 3 %count = count + 1; count = 1; %迭代完成，输出最优值 if stop == 1; [best, best_index]= max(fitval_current); sys = D_out(best_index); return; end %判断是否迭代完成 if mg > maxgen stop = 1; end if mg == 0 % 第一代，只进行迭代，然后计算各自功率,作为初始比较功率fitval_current if i == 0 i= i + 1; sys = D_out_current(1); elseif i > 1 && i < NP fitval_current(i-1) = adaptfunc(D_out_current(i-1),Uin); %记录第一代的上一个个体的功率 Pbest(i-1) = D_out_current(i-1); i= i + 1; sys = D_out_current(i); elseif i == NP fitval_current(i-1) = adaptfunc(D_out_current(i-1),Uin); Pbest(i-1) = D_out_current(i-1); i= i + 1; sys = D_out_current(NP); elseif i == NP + 1 fitval_current(i-1) = adaptfunc(D_out_current(i-1),Uin); Pbest(i-1) = D_out_current(i-1); [best, best_index]= max(fitval_current); Gbest = D_out_current(best_index); i = 1; Nc_count = Nc_count + 1; Nre_count = Nre_count + 1; mg = mg + 1; for j = 1 : NP %第一代种群中个体进行游动 MoveStep_PSO = c1*r1*(Pbest(j)-D_out_current(j)) + c2*r2*(Gbest-D_out_current(j)); D_out(j) = D_out_current(j)+ MoveStep(j) + MoveStep_PSO; if D_out(j) <= XL D_out(j) = XL+0.001; elseif D_out(j) >= XU D_out(j) = XU-0.001; end end sys = D_out(1); else i= i + 1; sys = D_out_current(i); end return; else %第一代之后 if i > 1 && i < NP fitval_new(i-1) =adaptfunc(D_out(i-1), Uin); %计算前一次占空比下的功率 Pbest(i-1) = D_out(i-1); i = i + 1; sys = D_out(i); elseif i == NP fitval_new(i-1) =adaptfunc(D_out(i-1), Uin); Pbest(i-1) = D_out(i-1); i = i + 1; sys = D_out(NP); elseif i == 1 i = i + 1; sys = D_out(i); elseif i == NP + 1 %种群迭代一次结束，开始进行新的一代种群繁殖 %趋向性操作,判定运动方向 fitval_new(i-1) =adaptfunc(D_out(i-1), Uin); Pbest(i-1) = D_out(i-1); r1 = rand(); r2 = rand(); for j = 1 : NP if (fitval_new(j) >= fitval_current(j)) D_out_current(j) = D_out(j); fitval_current(j) = fitval_new(j); else MoveStep(j) = - MoveStep(j); %若该方向功率未改进，说明不适应生存，改变方向运动 end end c1 = c1/3; c2 = c2/3; D_out(:) %打印最新的占空比 [best, best_index]= max(fitval_current); %计算最大功率点，best为最大功率，best_index为最大功率点在种群中的位置 mg D_out(best_index) %显示最大功率点的占空比 %Gbest = U(best_index) %显示最大功率点的电压 %Gmaxval = best %显示最大功率点的功率 best fit_order = order(fitval_current,NP) %显示当前种群功率从大到小顺序 Gbest = D_out(best_index); % mutation if Nc_count == Nc %进行满Nc次趋向性操作 Nc_count = 0; Nre_count = Nre_count + 1; %复制操作 MaxStep = MaxStep/2; MoveStep = unifrnd(-MaxStep,MaxStep,1,NP); %每Nc次繁殖生成一次新的随机步长 [fitval_current,D_out_current]=Reproduction(fitval_current,D_out_current,NP); %复制 D_out=D_out_current; if Nre_count == Nre %进行满Nre次复制操作后迁移操作 Nre_count = 0; for j=1:NP if(rand(0,1)<Ped) fitval_current = 0; %MoveStep(i) = unifrnd(-MaxStep,MaxStep); D_out(j) = unifrnd(XL,XU); end end end else Nc_count = Nc_count + 1; for j=1:NP MoveStep_PSO = c1*r1*(Pbest(j)-D_out(j)) + c2*r2*(Gbest-D_out(j)); D_out(j) = D_out(j) + MoveStep(j) + MoveStep_PSO; if D_out(j) <= XL D_