基于多级适应方法的无人机(UAV)在发动机输出情况下的导航和路径规划(Matlab代码实现)

简介: 基于多级适应方法的无人机(UAV)在发动机输出情况下的导航和路径规划(Matlab代码实现)

💥1 概述

文献来源:

@article{gu2022multi,
  title={Multi-level Adaptation for Automatic Landing with Engine Failure under Turbulent Weather},
  author={Gu, Haotian and Jafarnejadsani, Hamidreza},
  journal={arXiv preprint arXiv:2209.04132},
  year={2022}
}


运行MASC示例

1.打开定义引擎输出纬度、经度和高度的程序。然后首先把飞机放到发动机坏掉的地方。然后单击右上角的停止按钮×平面窗口的角落。

2在故障位置配置块中设置发动机输出全局位置


3.在simulink框架中设置机场坐标

4.单击运行按钮,首先启动模拟模型。


📚2 运行结果

部分代码:

tic
e = zeros(1,8000);
c = zeros(1,8000);
aileron_e = zeros(1,8000);
psi_ref = zeros(1,8000);
gamma_ref = zeros(1,8000);
%% This function is for testing for converge to planned straight line
xb = 31018;
yb = -23100;
xf = 34018;
yf = -27100;
Rl = 1016; % R value can not be small otherwise, the path following result is not good
psif = 0;
xl         =   xf + 4 * Rl * cos(psif - pi);
yl         =   yf + 4 * Rl * sin(psif - pi);
xu = xl + Rl * cos(psif - pi);
yu = yl + Rl * sin(psif - pi);
%Ru         =   sqrt((xl + Rl * cos(psif - pi) - xi)^2 + (yl + Rl * sin(psif - pi) - yi)^2);
%thetau     =   atan2( yi - yl - Rl * sin(psif - pi), xi - xl - Rl * cos(psif - pi));
%%
r = Rl; %radius of loiter curve
O = [xl yl]; %center of loiter or circular orbit
g = 9.81;%gravitational acceleration
%p = [curr_x curr_y];
p = [96900/3.2808 -84870/3.2808]; %UAV start position
psi = 4; %start heading
delta = 0.3; %look ahead position
%^^^^^^^^^^^^^^^^definition of controller parameters^^^^^^^^^^^^^^^^^^^^^^^
k_p=0.8;    %proportional gain
k_i=0.01;   %integral gain
k_d=1;      %derivative gain
%^^^^^^^^^^^^^^^^^^Specification of time step^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
dt=0.1  % this is a time unit which shoud match simulator x-plane
U_0=400; %initial UAV speed
U_d=435.9;%desired UAV speed
theta = atan2((p(2)-O(2)),(p(1)-O(1)));%Calculation of LOS angle
%^^^^^^^^^^^^^^^^^^^^^^^Definition of the look ahead point^^^^^^^^^^^^^^^^^
x_i = ((r*(cos(theta+delta)))+O(1));
y_i = ((r*(sin(theta+delta)))+O(2));
psi_d = atan2((y_i-p(2)),(x_i-p(1))); %commanded heading angle
u = (psi_d-psi); %controller input for changing heading angle
%^^^^^^^^^^^^^^^^^^^^^^^^^^^Motion of UAV^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
x_d=U_0*(cos(psi_d))*dt;
y_d=U_0*(sin(psi_d))*dt;
%^^^^^^^^^^^^^^^estimation of heading angle and position^^^^^^^^^^^^^^^^^^^
P_new = [(p(1)+x_d),(p(2)+y_d)];
psi_new = (psi+u);
%^^^^^^^^^^^^^^^^^^^^^over time positioning and heading of UAV^^^^^^^^^^^^^
X=[p(1)];
Y=[p(2)];
S=511; %area of  UAV wing
rho=0.3045; %density of air
b=59.64; %span of wing
mass = 333400  %mass of UAV 
I_xx=0.247e8; %inertial moment
L_p=-1.076e7; %rolling moment
Cl_da=0.668e-2;  %roll moment due to aileron deflection coefficient
Q_dS=1/2*rho*U_0^2*S; %dynamic pressure
L_da=Q_dS*b*Cl_da; %roll moment due to aileron
%^^^^^^^^^^^^^^^^^^^^^^^^^initialising controller^^^^^^^^^^^^^^^^^^^^^^^^^^
roll_ref=0; %initial UAV roll position
rollrate_ref=0; %initial UAV rollrate
t_ei=0; %thrust PI integrator
ei=0; %aileron PID integrator
%^^^^^^^^^^^^^^^^^^^estimation of stability derivatives^^^^^^^^^^^^^^^^^^^^
a=L_p/I_xx;
beta=L_da/I_xx;
roll_d=atan(u*U_0/g); %desired roll calculation
if abs(roll_d) > 1.5;
   if roll_d < 0;
      roll_d = -1.5;
   else if roll_d>0;
      roll_d = 1.5;
       end
   end
end
rollrate_d=roll_d*dt; %desired rollrate
aileron = k_p*(roll_d-roll_ref)+(k_i*ei)+k_d*(rollrate_d-rollrate_ref);
%deflection of aileron
rollrate_new = (((a*rollrate_ref)+(beta*aileron))*dt); %new roll rate output
roll_new = (rollrate_new/dt)+roll_ref; %new roll output
roll_old=roll_ref; %initialising old roll for feedback
rollrate_old=rollrate_ref; %initiallising old rollrate for feedback
%^^^^^^^^^^^^^^^^^^^^^^^^^control of thurst^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
t_ei=t_ei+(U_d-U_0)*dt;
thrust=k_p*(U_d-U_0)+(k_i*t_ei);
V_new=U_0+(thrust*dt);
V_old=V_new;
count = 0
while count<1000
Ru = abs ((((P_new(1) - O(1))^2) + ((P_new(2) - O(2))^2)) ^(1/2)-r);
%Calculation of UAV distance from center
theta_new = atan2((P_new(2)-O(2)),(P_new(1)-O(1))); %new path angle calculation
x_i = ((r*(cos(theta_new+delta)))+O(1));
y_i = ((r*(sin(theta_new+delta)))+O(2));
psi_d = atan2((y_i-P_new(2)),(x_i-P_new(1)));%calculation of desired heading angle
u = wrapToPi(psi_d-psi_new); %controller input for changing heading angle
ei=ei+((roll_d-roll_old)*dt); %updating the integrator
roll_d=atan(u*V_old/g); %desired roll calculation
if abs(roll_d) > 1.5; %limit of roll
    if roll_d < 0;
       roll_d = -1.5;
    else if roll_d>0;
       roll_d = 1.5;
        end
    end
end
rollrate_d=(roll_d-roll_old)*dt; %calculation of desired rollrate
aileron = (k_p*(roll_d-roll_old)+(k_i*ei)+(k_d*(rollrate_d-rollrate_old))); %calculation of deflection of aileron
rollrate_new = (((a*rollrate_old)+(beta*aileron))*dt); %new rollrate calculation
roll_new = (rollrate_new/dt)+roll_old; %new roll angle calculation
rollrate_old=rollrate_new; %rollrate as feedback
roll_old=roll_new; %roll angle as feedback
psi_old = psi_new; %UAV heading as feedback
psi_b=g/V_old*(tan(roll_new));
%due to new roll change in heading
psi_new = wrapToPi(psi_new+psi_b); %calculation of new heading angle
gamma_new = -15*pi/180;
Q_dS=1/2*rho*V_old^2*S; %calculation of dynamic pressure
L_da=Q_dS*b*Cl_da;
%due to aileron calculation of roll moment
beta=L_da/I_xx;
a=L_p/I_xx;
%^^^^^^^^^^^^^^^^^^^^^Calculation of UAV movements^^^^^^^^^^^^^^^^^^^^^^^^^
x_d=V_old*(cos(psi_new))*dt;
y_d=V_old*(sin(psi_new))*dt;
P_new = [(P_new(1)+x_d) (P_new(2)+y_d)];
%^^^^^^^^^^^^^^^^^^^^^^^^^contorl of thrust^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
t_ei=t_ei+(U_d-V_old)*dt;
thrust=k_p*(U_d-V_old)+(k_i*t_ei);
V_new=V_old+(thrust*dt);
V_old=V_new;
figure(1)
Y=[ Y P_new(2)];
X=[ X P_new(1)];
plot(X,Y)
hold on
Q = 0 : 0.01 : 2*pi;
W_c = (r * (cos(Q)))+O(1);
A_c = (r * (sin(Q)))+O(2);
plot(W_c,A_c,':')
xlim([xl-2*Rl xl+2*Rl])
ylim([yl-2*Rl yl+2*Rl])
xlabel('x-direction in ft')
ylabel('y-direction in ft')
title('Followed path using carrot chasing algorithm')
drawnow
count = count+1
hold on
for j = count;
%array of measurements
d = (abs(((O(1)-P_new(1))^2)+((O(2)-P_new(2))^2))^(1/2)) ;
e(1,j) = u;
c(1,j) = Ru;
aileron_e(1,j) = aileron;
if psi_d >=0
    psi_ref(1,j) = psi_d;
elseif psi_new < 0
    psi_ref(1,j) = psi_d+2*pi;
end
%psi_ref(1,j) = psi_new;
DesiredHeading = psi_ref(1,j);
disp(DesiredHeading)
gamma_ref(1,j) = gamma_new;
DesiredFlightPath = gamma_ref(1,j);
disp(DesiredFlightPath)
end
hold off
end
toc
%^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^measurment plots^^^^^^^^^^^^^^^^^^^^^^^^^^^^
figure(2)
f = [1:1:count];
plot(f,e)
xlabel('time in (sec/100)')
ylabel('Change in heading in radian')
title('Variation in controller effort with time')
figure(3)
plot(f,c)
xlabel('time in (sec/100)')
ylabel('cross track deviation(ft)')
title('Variation of cross track deviation with time')
figure(4)
plot(f,aileron_e)
xlabel('time in (sec/100)')
ylabel('Deflection of aileron in radian')
title('Variation in aileron control with time')
figure(5)
plot(f,psi_ref)
xlabel('time in (sec/100)')
ylabel('heading new in radian')
title('Variation in controller effort with time')
figure(6)
plot(f,gamma_ref)
xlabel('time in (sec/100)')
ylabel('pitch angle in radian')
title('Variation in controller effort with time')
time=count*dt


🎉3 参考文献

部分理论来源于网络,如有侵权请联系删除。

@article{gu2022multi,
  title={Multi-level Adaptation for Automatic Landing with Engine Failure under Turbulent Weather},
  author={Gu, Haotian and Jafarnejadsani, Hamidreza},
  journal={arXiv preprint arXiv:2209.04132},
  year={2022}
}


🌈4 Matlab代码实现

相关文章
|
9天前
|
存储 编解码 算法
【多光谱滤波器阵列设计的最优球体填充】使用MSFA设计方法进行各种重建算法时,图像质量可以提高至多2 dB,并在光谱相似性方面实现了显著提升(Matlab代码实现)
【多光谱滤波器阵列设计的最优球体填充】使用MSFA设计方法进行各种重建算法时,图像质量可以提高至多2 dB,并在光谱相似性方面实现了显著提升(Matlab代码实现)
|
9天前
|
机器学习/深度学习 传感器 算法
【高创新】基于优化的自适应差分导纳算法的改进最大功率点跟踪研究(Matlab代码实现)
【高创新】基于优化的自适应差分导纳算法的改进最大功率点跟踪研究(Matlab代码实现)
91 14
|
9天前
|
机器学习/深度学习 算法
【概率Copula分类器】实现d维阿基米德Copula相关的函数、HACs相关的函数研究(Matlab代码实现)
【概率Copula分类器】实现d维阿基米德Copula相关的函数、HACs相关的函数研究(Matlab代码实现)
|
9天前
|
机器学习/深度学习 传感器 算法
【裂纹检测】检测和标记图片中的裂缝(Matlab代码实现)
【裂纹检测】检测和标记图片中的裂缝(Matlab代码实现)
|
10天前
|
算法 计算机视觉
【MPDR & SMI】失配广义夹角随输入信噪比变化趋势、输出信干噪比随输入信噪比变化趋势研究(Matlab代码实现)
【MPDR & SMI】失配广义夹角随输入信噪比变化趋势、输出信干噪比随输入信噪比变化趋势研究(Matlab代码实现)
|
10天前
|
编解码 人工智能 算法
【采用BPSK或GMSK的Turbo码】MSK、GMSK调制二比特差分解调、turbo+BPSK、turbo+GMSK研究(Matlab代码实现)
【采用BPSK或GMSK的Turbo码】MSK、GMSK调制二比特差分解调、turbo+BPSK、turbo+GMSK研究(Matlab代码实现)
|
10天前
|
机器学习/深度学习 编解码 并行计算
【改进引导滤波器】各向异性引导滤波器,利用加权平均来实现最大扩散,同时保持图像中的强边缘,实现强各向异性滤波,同时保持原始引导滤波器的低低计算成本(Matlab代码实现)
【改进引导滤波器】各向异性引导滤波器,利用加权平均来实现最大扩散,同时保持图像中的强边缘,实现强各向异性滤波,同时保持原始引导滤波器的低低计算成本(Matlab代码实现)
|
10天前
|
机器学习/深度学习 传感器 边缘计算
【故障诊断】基于时滞反馈随机共振的增强型旋转电机故障诊断(Matlab代码实现)
【故障诊断】基于时滞反馈随机共振的增强型旋转电机故障诊断(Matlab代码实现)
|
10天前
|
传感器 机器学习/深度学习 算法
【UASNs、AUV】无人机自主水下传感网络中遗传算法的路径规划问题研究(Matlab代码实现)
【UASNs、AUV】无人机自主水下传感网络中遗传算法的路径规划问题研究(Matlab代码实现)
|
10天前
|
运维 算法
【故障诊断】基于最小熵反卷积、最大相关峰度反卷积和最大二阶环平稳盲反卷积等盲反卷积方法在机械故障诊断中的应用研究(Matlab代码实现)
【故障诊断】基于最小熵反卷积、最大相关峰度反卷积和最大二阶环平稳盲反卷积等盲反卷积方法在机械故障诊断中的应用研究(Matlab代码实现)

热门文章

最新文章