?????目錄
??1 概述
??2 運行結(jié)果
??3 參考文獻
?????4 Matlab代碼
??1 概述
自適應(yīng)巡航控制技術(shù)為目前由于汽車保有量不斷增長而帶來的行車安全、駕駛舒適性及交通擁堵等問題提供了一條有效的解決途徑，因此本文通過理論分析、仿真驗證及實車實驗對自適應(yīng)巡航控制中的若干關(guān)鍵技術(shù)展開研究，以提高自適應(yīng)巡航控制在不同工況下的應(yīng)用能力。??
本研究為基于預(yù)測控制模型的自適應(yīng)巡航控制仿真與機器人實現(xiàn)。
研究目的：
- 在兩輛車之間已經(jīng)達到了近乎精確的縱向模型
- 試圖使控制響應(yīng)接近可行性和真實條件。
- 滿足防撞和保持安全距離，前車為主要目標，舒適性為次要目標。（控制應(yīng)用于以下汽車）
- 在 MATLAB 上應(yīng)用實現(xiàn)和仿真。
??2 運行結(jié)果
主函數(shù)部分代碼：
clear ;
close all;
clc
%
Define the sample time, |Ts|, and simulation duration, |t|, in seconds.
t0 = 0;
Ts = 0.1;
Tf = 100;
t = t0:Ts:Tf;
Nt = numel(t);
% Specify the initial position and velocity for the two vehicles.
%
x0_lead = 0; %Initial position of lead car (m)
%v0_lead = 0; %Initial velocity of lead car (m/s)
%
x0_ego = 0; %Initial position of ego car (m)
%v0_ego = 0; %Initial velocity of ego car (m/s)
%
The safe distance between the lead car and the ego car is a function
% of the ego car velocity, $V_{ego}$:
%
% $$ D_{safe} = D_{default} + T_{gap}\times V_{ego} $$
%
% where $D_{default}$ is the standstill default spacing and $T_{gap}$ is
% the time gap between the vehicles. Specify values for $D_{default}$, in
% meters, and $T_{gap}$, in seconds.
t_gap = 1.4;
D_default = 10;
%
Specify the driver-set velocity in m/s.
v_set = 30;
%
Considering the physical limitations of the vehicle dynamics, the
% acceleration is constrained to the range |[-3,2]| (m/s^2).
a_max = 2; da_max = 0.15;
a_min = -3; da_min = -0.2;
?
?
%
the relationship between the actual acceleration and the desired
% acceleration of the host vehicle satisfies the following conditions
%
% $$ a(k+1) = (1-\frac{Ts}{\tau}) \times a(k) + \frac{Ts}{\tau} \times u(k)$$
%
% where $ \tau $ is the time lag of the ACC system
tau = 0.3;
?
%
Np = 20 ; % Prediction Horizon
%Nc = 20 ; % Control Horizon
%
% Examples
% In this section we want to try to specify the various parameters
% of the machine for different simulation
?
%
EX.1
% N = 5;
% Np = 20 ; % Prediction Horizon
% Nc = 5 ; % Control Horizon
% x0_ego = 0;
% v0_ego = 0;
% x0_lead = 50;
% v0_lead = 15;
% a_lead = 0.3*sin(2*pi*0.03*t); % Acceleration of lead car is a disturbance for our plant;
% [lead_car_position , lead_car_velocity] = lead_car_simulation(x0_lead,v0_lead,a_lead,t,Ts ,tau);
%
EX.2
N = 5;
Np = 20 ; % Prediction Horizon
Nc = 15 ; % Control Horizon
x0_ego = 0;
v0_ego = 0;
x0_lead = 20;
v0_lead = 5;
a_lead = [1*(1-exp(-0.5*t(1:floor(Nt/5)))) ,0.5+0.5*exp(-0.5*t(1:floor(Nt/5))) , -0.5+exp(-0.5*t(1:floor(Nt/5))) ,-0.5*exp(-0.5*t(1:floor(Nt/5))) , zeros(1,floor(Nt/5)+1)];
[lead_car_position , lead_car_velocity] = lead_car_simulation(x0_lead,v0_lead,a_lead,t,Ts ,tau);
%
% % EX.3
% Np = 20 ; % Prediction Horizon
% Nc = 15 ; % Control Horizon
% x0_ego = 0;
% v0_ego = 0;
% x0_lead = 1500;
% v0_lead = 0;
% a_lead = zeros(1,Nt) ;
% [lead_car_position , lead_car_velocity] = lead_car_simulation(x0_lead,v0_lead,a_lead,t,Ts ,tau);
%
% Car State Space Model
?
Am=[1 Ts 0.5*Ts^2
0 1 Ts
0 0 1-Ts/tau ];
?
Bm=[0 ; 0 ; Ts/tau];
?
Cm=[1 0 0
0 1 0];
?
?
n = size(Am , 1) ; % number of eigenvalues
q = size(Cm , 1) ; % number of outputs
m = size(Bm , 2) ; % number of inputs
?
?
[A , B , C] = AugemenFun(Am , Bm , Cm) ;
?
a = 0.5 ;
[Al , L0] = LagFun(N,a);
L = zeros( N , Nc );
L( : , 1) = L0 ;
for i = 2:Nc
L(:,i) = Al*L(: , i-1) ;
end
??3 參考文獻
[1]李朋,魏民祥,侯曉利.自適應(yīng)巡航控制系統(tǒng)的建模與聯(lián)合仿真[J].汽車工程,2012,34(07):622-626.文章來源：http://www.zghlxwxcb.cn/news/detail-570076.html
部分理論引用網(wǎng)絡(luò)文獻，若有侵權(quán)聯(lián)系博主刪除。文章來源地址http://www.zghlxwxcb.cn/news/detail-570076.html
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