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⛄ 内容介绍
我们将重点放在了无人机外环控制上。这一部分我们采用了传统的PID控制模块对无人机飞行进行控制。PID的原型可以表示为：
它是一个根据反馈误差来修正输出函数的过程。K0，K1，K2这三个参数可以为0，那么就形成了PI、PD、ID、PID这几种形式。一般PID、PD这两种用的比较多。而我们使用的为PD。
在确定好航线之后，我们可以得到无人机巡航仿真路线图，位移图与速度图如下：
⛄ 部分代码
function [t_out, s_out, QP] = simulation_3d_new(trajhandle, controlhandle)
%[t_out, s_out] = simulation_3d(trajhandle, controlhandle)
% NOTE: This srcipt will not run as expected unless you fill in proper
% code in trajhandle and controlhandle
% You should not modify any part of this script except for the
% visualization part
%
% ***************** QUADROTOR SIMULATION *****************
% *********** YOU SHOULDN'T NEED TO CHANGE ANYTHING BELOW **********
addpath('utils');
% real-time
real_time = true;
% max time
max_time = 100;
% parameters for simulation
params = sys_params;
%% **************************** FIGURES *****************************
disp('Initializing figures...');
h_fig = figure;
h_3d = gca;
axis equal
grid on
%view(3);
view(48.8, 25.8);
xlabel('x [m]'); ylabel('y [m]'); zlabel('z [m]')
quadcolors = lines(1);
set(gcf,'Renderer','OpenGL')
%% *********************** INITIAL CONDITIONS ***********************
disp('Setting initial conditions...');
tstep = 0.01; % this determines the time step at which the solution is given
cstep = 0.05; % image capture time interval
max_iter = max_time/cstep; % max iteration
nstep = cstep/tstep;
time = 0; % current time
err = []; % runtime errors
% Get start and stop position
des_start = trajhandle(0, []);
des_stop = trajhandle(inf, []);
stop_pos = des_stop.pos;
x0 = init_state(des_start.pos, 0);
xtraj = zeros(max_iter*nstep, length(x0));
ttraj = zeros(max_iter*nstep, 1);
x = x0; % state
pos_tol = 0.01;
vel_tol = 0.01;
%% ************************* RUN SIMULATION *************************
disp('Simulation Running....');
% Main loop
for iter = 1:max_iter
timeint = time:tstep:time+cstep;
tic;
% Initialize quad plot
if iter == 1
QP = QuadPlot(1, x0, 0.1, 0.04, quadcolors(1,:), max_iter, h_3d);
current_state = stateToQd(x);
desired_state = trajhandle(time, current_state);
QP.UpdateQuadPlot(x, [desired_state.pos; desired_state.vel], time);
h_title = title(sprintf('iteration: %d, time: %4.2f', iter, time));
end
% Run simulation
[tsave, xsave] = ode45(@(t,s) quadEOM(t, s, controlhandle, trajhandle, params), timeint, x);
x = xsave(end, :)';
% Save to traj
xtraj((iter-1)*nstep+1:iter*nstep,:) = xsave(1:end-1,:);
ttraj((iter-1)*nstep+1:iter*nstep) = tsave(1:end-1);
% Update quad plot
current_state = stateToQd(x);
desired_state = trajhandle(time + cstep, current_state);
QP.UpdateQuadPlot(x, [desired_state.pos; desired_state.vel], time + cstep);
set(h_title, 'String', sprintf('iteration: %d, time: %4.2f', iter, time + cstep))
time = time + cstep; % Update simulation time
t = toc;
% Check to make sure ode45 is not timing out
if(t> cstep*50)
err = 'Ode45 Unstable';
break;
end
% Pause to make real-time
if real_time && (t < cstep)
pause(cstep - t);
end
% Check termination criteria
if terminate_check(x, time, stop_pos, pos_tol, vel_tol, max_time)
break
end
end
%% ************************* POST PROCESSING *************************
% Truncate xtraj and ttraj
xtraj = xtraj(1:iter*nstep,:);
ttraj = ttraj(1:iter*nstep);
% Truncate saved variables
QP.TruncateHist();
% Plot position
h_pos = figure('Name', ['Quad position']);
plot_state(h_pos, QP.state_hist(1:3,:), QP.time_hist, 'pos', 'vic');
plot_state(h_pos, QP.state_des_hist(1:3,:), QP.time_hist, 'pos', 'des');
% Plot velocity
h_vel = figure('Name', ['Quad velocity']);
plot_state(h_vel, QP.state_hist(4:6,:), QP.time_hist, 'vel', 'vic');
plot_state(h_vel, QP.state_des_hist(4:6,:), QP.time_hist, 'vel', 'des');
if(~isempty(err))
% error(err);
end
disp('finished.')
t_out = ttraj;
s_out = xtraj;
end
⛄ 运行结果
图中蓝线为理想状况下无人机飞行路线情况以及参数情况，红线为在考虑重力与呈正态分布的干扰噪声环境下无人机的路线情况以及参数情况。
在此基础上，为了方便初学者对于无人机飞行情况真实把握以及感受PID参数调节对无人机的影响，我们建立了用户交互界面。用户可以自主调节PID参数并自主控制无人机飞行，通过这种方式来模拟无人机飞行的真实情况，并了解PID参数是如何影响无人机飞行状态的。
⛄ 参考文献
[1] 文锟.基于计算动词PID控制器的四旋翼无人机控制及地面控制系统设计[J]. 2013.
[2] 穆鲁肯.无人机控制器的建模与比较研究[D].大连理工大学,2016.
[3] 邵霖文,廖芳,丁黎明,等.基于MATLAB的四旋翼无人机飞控PID仿真设计[J].山西电子技术, 2021(005):000.
