【PID优化】基于麻雀算法PID控制器优化设计含Matlab源码

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本文介绍了一种基于麻雀算法的PID控制器参数自整定方法，通过将群智能技术应用于PID控制系统的参数搜索，实现了控制性能的优化。作者分享了使用SSA算法的具体代码实现过程，并展示了通过仿真得到的优化效果，对比了传统方法，突显了新兴算法在控制领域的优势。

1 简介

PID控制器的参数整定表现为在3个参数空间中寻求最优值,使得系统的控制性能达到最优。麻雀算法是一种新兴的群智能优化算法。为加强PID控制器的参数整定,基于麻雀算法,提出一种PID控制器参数自整定方法,实现PID控制器的参数寻优。

2 部分代码

function [fMin , bestX, Convergence_curve] = SSA(X, N, M, c, d, dim, fobj)P_percent = 0.2;    % 发现者的种群规模占总种群规模的百分比pNum = round(N*P_percent);    % 发现者数量20%SD = pNum/2;      % 警戒者数量10%ST = 0.8;           % 安全阈值lb = c.*ones(1, dim);     % 下限ub = d.*ones(1,dim);    % 上限% 初始化for i = 1:N%     X(i, :) = lb + (ub - lb) .* rand(1, dim);    fitness(i) = fobj(X(i, :));endpFit = fitness;pX = X;                            % 与pFit相对应的个体最佳位置[fMin, bestI] = min(fitness);      % fMin表示全局最优解bestX = X(bestI, :);             % bestX表示全局最优位置%% 迭代寻优for t = 1 : M           [~, sortIndex] = sort(pFit);            % 排序        [fmax, B] = max(pFit);    worst = X(B, :);        %% 发现者位置更新    r2 = rand(1);    if r2 < ST        for i = 1:pNum      % Equation (3)            r1 = rand(1);            X(sortIndex(i), :) = pX(sortIndex(i), :)*exp(-(i)/(r1*M));            X(sortIndex(i), :) = Bounds(X(sortIndex(i), :), lb, ub);            fitness(sortIndex(i)) = fobj(X(sortIndex(i), :));        end    else        for i = 1:pNum            X(sortIndex(i), :) = pX(sortIndex(i), :)+randn(1)*ones(1, dim);            X(sortIndex(i), :) = Bounds(X(sortIndex(i), :), lb, ub);            fitness(sortIndex(i)) = fobj(X(sortIndex(i), :));        end    end        [~, bestII] = min(fitness);    bestXX = X(bestII, :);        %% 跟随者位置更新    for i = (pNum+1):N                     % Equation (4)        A = floor(rand(1, dim)*2)*2-1;        if i > N/2            X(sortIndex(i), :) = randn(1)*exp((worst-pX(sortIndex(i), :))/(i)^2);        else            X(sortIndex(i), :) = bestXX+(abs((pX(sortIndex(i), :)-bestXX)))*(A'*(A*A')^(-1))*ones(1, dim);        end        X(sortIndex(i), :) = Bounds(X(sortIndex(i), :), lb, ub);        fitness(sortIndex(i)) = fobj(X(sortIndex(i), :));    end        %% 警戒者位置更新    c = randperm(numel(sortIndex));    b = sortIndex(c(1:SD));    for j = 1:length(b)      % Equation (5)        if pFit(sortIndex(b(j))) > fMin            X(sortIndex(b(j)), :) = bestX+(randn(1, dim)).*(abs((pX(sortIndex(b(j)), :) -bestX)));        else            X(sortIndex(b(j)), :) = pX(sortIndex(b(j)), :)+(2*rand(1)-1)*(abs(pX(sortIndex(b(j)), :)-worst))/(pFit(sortIndex(b(j)))-fmax+1e-50);        end        X(sortIndex(b(j)), :) = Bounds(X(sortIndex(b(j)), :), lb, ub);        fitness(sortIndex(b(j))) = fobj(X(sortIndex(b(j)), :));    end        for i = 1:N        % 更新个体最优        if fitness(i) < pFit(i)             pFit(i) = fitness(i);            pX(i, :) = X(i, :);        end        % 更新全局最优        if pFit(i) < fMin            fMin = pFit(i);            bestX = pX(i, :);        end    end    Convergence_curve(t) = fMin;        disp(['SSA: At iteration ', num2str(t), ' ,the best fitness is ', num2str(fMin)]);end%% 边界处理function s = Bounds(s, Lb, Ub)% 下界temp = s;I = temp < Lb;temp(I) = Lb(I);% 上界J = temp > Ub;temp(J) = Ub(J);% 更新s = temp;