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智能优化算法 神经网络预测 雷达通信 无线传感器 电力系统
信号处理 图像处理 路径规划 元胞自动机 无人机
⛄ 内容介绍
无人机在军事领域有着重要的战略地位,可用于侦查,打击等.实际应用中需要用到多无人机对目标群完成侦查任务,便要先进行任务规划和航迹规划.本文优化路径的生成的任务计划减少功耗的无人驾驶飞机。
⛄ 部分代码
clc
close all;
tc=cputime;
global J m M e1 e2 e3 Pr La g p ki kp pt kt
h=.01; % Step size
tf=30; % Final time for simulation
t0=0;
e1=[1;0;0];e2=[0;1;0];e3=[0;0;1];
% UAV parameters
J=diag([0.0008,0.0008,0.0014]); % kgm^2; UAV inertia; 3 x 3 matrix
m=0.8004; % kg, mass of UAV
M=m*eye(3); % Mass matrix
g=9.81; % Gravity
% Initialization
% R0=[1 0 0;0 1 0;0 0 1];
R0 = [-0.8487 0 -0.5288;
0.4197 0.6083 -0.6736;
0.3217 -0.7937 -0.5163];
b0=[0;0;0]; % initial position
Om0=[0 0 0]'; % initial angular velocity
nu0=[0 0 0]';
% initial translational velocity
vd0=[0;0;0]; % initial desired translational velocity
dvd0=[0;0;0]; % initial desired translational acceleration
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Gains for the sine trajectory
% kt = 0.75;%less than 1 always
% Pr = eye(3);
% pt = 1.2;
%
% Attitude Gain
% La = 0.025*eye(3); % lower the better for trq
% p = 1.15; % 1.1; p>1
% ki = 3.4;
% kp = 0.01;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %new gains modified by somesh for sine trajectory for 1200 m distance range
% kt = 0.95;%less than 1 always
% Pr = 40*eye(3);
% pt = 1.2;
%
% % Attitude Gain
% La = 0.05*eye(3); % lower the better for trq
% p = 0.98; % 1.1; p>1 %lower the better for initial torque
% ki = 3.35;
% kp = 0.01;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%gains for cosine trajectory
kt = 0.25;%less than 1 always
Pr = 22*eye(3);
pt = 1.0;
%
% % Attitude Gain
La = 0.015*eye(3); % lower the better for trq
p = 1.3; % 1.1; p>1
ki = 3.25;
kp = 0.01;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[t,R,Rd,b,fm,tau,tau1,tau2,tau3,tau4,nu,Omd,dOmd,Om,Q,bd,vd,dvd,bt,vt] = LGVI_SE3_UAV(b0,R0,nu0,Om0,dvd0,t0,tf,h);
% Plots the results
Results_CUSE(t,bd,b,bt,vt,R,Q,fm,tau,Rd,Omd,dOmd);
⛄ 运行结果
