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.".
5 MATLAB代码与数据下载地址
见博客主页
