1 简介

SSA主要模拟了麻雀觅食的过程。麻雀觅食过程是发现者-跟随者模型的一种，同时还叠加了侦查预警机制。麻雀中容易找到食物的个体作为发现者，其他个体作为跟随者，同时种群中选取一定比例的个体进行侦查预警，如果发现危险则放弃食物，安全第一。由文献可知，SSA 是一种优于GWO、PSO、引力搜索算法（Gravity Search Algorithm，GSA）等算法的一种新的群智能优化算法。SSA算法中有发现者、追随者以及警戒者，分别按照各自规则进行位置更新，更新规则如下：

2 部分代码

function sdot = quadEOM_readonly(t, s, F, M, params)
% QUADEOM_READONLY Solve quadrotor equation of motion
%   quadEOM_readonly calculate the derivative of the state vector
%
% INPUTS:
% t     - 1 x 1, time
% s     - 13 x 1, state vector = [x, y, z, xd, yd, zd, qw, qx, qy, qz, p, q, r]
% F     - 1 x 1, thrust output from controller (only used in simulation)
% M     - 3 x 1, moments output from controller (only used in simulation)
% params - struct, output from nanoplus() and whatever parameters you want to pass in
%
% OUTPUTS:
% sdot   - 13 x 1, derivative of state vector s
%
% NOTE: You should not modify this function
% See Also: quadEOM_readonly, nanoplus

%************ EQUATIONS OF MOTION ************************
% Limit the force and moments due to actuator limits
A = [0.25,                      0, -0.5/params.arm_length;
    0.25,  0.5/params.arm_length,                      0;
    0.25,                      0,  0.5/params.arm_length;
    0.25, -0.5/params.arm_length,                      0];

prop_thrusts = A*[F;M(1:2)]; % Not using moment about Z-axis for limits
prop_thrusts_clamped = max(min(prop_thrusts, params.maxF/4), params.minF/4);

B = [                 1,                 1,                 1,                  1;
                     0, params.arm_length,                 0, -params.arm_length;
    -params.arm_length,                 0, params.arm_length,                 0];
F = B(1,:)*prop_thrusts_clamped;
M = [B(2:3,:)*prop_thrusts_clamped; M(3)];

% Assign states
x = s(1);
y = s(2);
z = s(3);
xdot = s(4);
ydot = s(5);
zdot = s(6);
qW = s(7);
qX = s(8);
qY = s(9);
qZ = s(10);
p = s(11);
q = s(12);
r = s(13);

quat = [qW; qX; qY; qZ];
bRw = QuatToRot(quat);
wRb = bRw';

% Acceleration
accel = 1 / params.mass * (wRb * [0; 0; F] - [0; 0; params.mass * params.grav]);

% Angular velocity
K_quat = 2; %this enforces the magnitude 1 constraint for the quaternion
quaterror = 1 - (qW^2 + qX^2 + qY^2 + qZ^2);
qdot = -1/2*[0, -p, -q, -r;...
            p,  0, -r,  q;...
            q,  r,  0, -p;...
            r, -q,  p,  0] * quat + K_quat*quaterror * quat;

% Angular acceleration
omega = [p;q;r];
pqrdot   = params.invI * (M - cross(omega, params.I*omega));

% Assemble sdot
sdot = zeros(13,1);
sdot(1) = xdot;
sdot(2) = ydot;
sdot(3) = zdot;
sdot(4) = accel(1);
sdot(5) = accel(2);
sdot(6) = accel(3);
sdot(7) = qdot(1);
sdot(8) = qdot(2);
sdot(9) = qdot(3);
sdot(10) = qdot(4);
sdot(11) = pqrdot(1);
sdot(12) = pqrdot(2);
sdot(13) = pqrdot(3);

end

3 仿真结果

4 参考文献

[1]刘贵云等. "基于混沌自适应麻雀搜索算法的无人机三维航迹规划方法.".