基于MPC的车辆自动泊车轨迹跟踪控制+代码
附赠自动驾驶学习资料和量产经验：链接
一、MPC理论推导
参考《无人驾驶模型预测控制》与《模型预测控制》
首先是最优控制，之前总结LQR是一种无约束的线性二次最优控制问题，通过求解Ricatti方程得到最优控制率，而对于非线性系统，求取最优控制的问题被转化为求解对于的Hamilton-Jacobi-Bellman方程，可以转换成求解有约束的优化问题。并且mpc起源于工业应用，就是先在工业界成功应用之后才有了相对应的理论研究。
任何一本mpc的书上基本都会写这段话
模型预测控制的基本思想就是利用已有的模型、 系统当前的状态和未来的控制量去预测系统未来的输出， 通过滚动地求解带约束优化问题来实现控制目的， 具有预测模型、 滚动优化和反馈校正三个特点 。
以及下面这句话
模型预测控制通常将待优化问题转换为二次型规划 ( Quadratic Program-ming， QP) 问题。二次型规划是一个典型的数学优化问题， 它的优化目标为带有线性或者非线性约束的二次型实函数，常用的解法为有效集法或者内点法。
龚建伟老师的书对于mpc的理论推导是按照如下的顺序
模型预测控制器的主体， 主要由线性误差模型、 系统约束以及目标函数组成。 线性误差模型是轨迹跟踪控制系统的数学描述， 也是构建控制算法的基础。 系统约束包括车辆执行机构约束、 控制量平滑约束以及车辆稳定性约束等。 目标函数的设计则综合考虑轨迹跟踪的快速性以及平稳性。
那么首先是线性误差模型，和LQR一样，直接给出线性化和离散化之后的车辆运动学模型，系统的状态变量是线性误差变量
然后龚建伟老师介绍了目标函数和系统约束，如下所示
可以看到，在目标函数中， 求解的变量为控制时域内的控制增量ΔU， 那么约束条件中也只能是以控制增量或者控制增量与转换矩阵相乘的形式出现。也就是说，相比于式LQR给出的目标函数，用控制增量取代控制量，并且加入了松弛因子。 这样不仅能对控制增量进行直接的限制， 也能防止执行过程中出现没有可行解的情况。 我们需要计算的，就是在目标函数中，未来一段时间系统的输出
书上给出的控制量和控制增量的约束如下，分别是对速度输出和前轮转角的约束
我自己理解的是可以把这个的目标函数当作一个新的状态方程的目标函数去看待，那么里面的η（k）和控制增量Δu（k）就对应这个新的状态空间方程的系统变量和输出，接下来的目标就是构建新的一个模型来满足这个目标函数（书中对这部分的推导在第三章）
构建下面这个新的状态变量，把已知的矩阵带进去，右下角就是构建的新的状态变量方程
可以写的清楚一点
里面什么a11，还是b11啥的都是原来的矩阵的符号啥的，Nx和Nu也就是状态量的个数和控制量的个数，Nx=3，Nu=2，A,B,C矩阵分别是我们新构建的状态空间表达式里面的状态矩阵、输入矩阵、输出矩阵
然后对新构建的状态变量kesi（k+1）和系统的输出η（k+1）展开看看有什么规律，这部分叫预测
里面写的Np和Nc也就是预测时域和控制时域了，为了使得关系更加明确。将系统未来时刻的输出写成矩阵的形式，对于输出方程η（k），化简合并得到
对于新构建出来的输出方程，可以看到，在预测时域内的状态量和输出量都可以通过新系统当前的状态和控制时域内的控制增量得到。
接下来把上面得到的输出方程带入到，目标函数里面看看，那么可以定义这个方程的参考值Yr，也就是参考的输出向量（其实是0，因为系统的状态量是误差，所以参考输出是0），化简一下（下面第一张图片的目标函数没有加松弛因子）
这部分我觉得要重点掌握，就是将优化问题转化成二次型规划QP问题（应该是这么个意思）
重新推导一下
书上的过程也贴一下，字母的具体的含义应该很好明白，方框内的可以化简掉
既然成了QP问题，那么就是能够约束控制量和控制量的增量，把控制输入展开写写
这部分的化简我觉得还得在理理思路，总之基于以上的思路，把最优化问题转化成了一个QP问题
二、代码模型
使用的软件版本分别是matlab2020b+carsim8.02
仿真场景是一个侧方停车的工况，在carsim中可以搭建道路环境，如下
由于是泊车，之前的模型是基于自行车模型推导的，所以要将计算出的前轮转角转化为车辆左右前轮的转角，也就是分别以左前轮和右前轮的转角输入，并且设置倒车，也就是倒挡-2，同时在车辆到达指定位置的时候，给一个制动压力Pbk
carsim中设定一个初始的车辆速度-2.88km/h，以及车辆初始位置，前轴的中心位置X0,Y0,YAW
可以把工况保存成一个cpar文件方便后续的使用，simulink模型如下
左边将自行车模型前轮转角转换成左右前轮转角的matlab function如下
function [delta_L,delta_R] = fcn(delta)
l=1.923;
L1=1.2;
tan_delta_L=1/(1/(tan(delta/180*pi))-(L1/2)/l);
tan_delta_R=1/(1/(tan(delta/180*pi))+(L1/2)/l);
delta_L=atan(tan_delta_L)/pi*180;
delta_R=atan(tan_delta_R)/pi*180;
原理如下
这里我不太明白，按公式推应该是和下面一样，L是轴距，L1是左轮到右轮的距离，和代码里面表示的形式差了一个负号，欢迎各位讨论（仿真的时候按代码的进行仿真）
右边的matlab function就是把车辆前轴中心的位置转换成后轴中心的位置，这个比较简单
function [Xr,Yr] = fcn(Xo,Yo,Yaw)
l=1.923;%轴距
Xr=Xo-l*cos(Yaw/180*pi);
Yr=Yo-l*sin(Yaw/180*pi);
下面分别介绍横向控制和纵向控制
Longitudinal control
纵向控制就是速度控制了，通过设定carsim中车辆初始速度来控制车速
就是用后轴中心的位置减去后轴lr减去一个误差e，如果这个值大于等于0，说明还没到规定的位置，那么不给制动压力，否则给一个6的制动压力
Lateral MPC control
横向控制的流程图如下
首先是左边的模块，遍历所有的参考点，找到当前时刻参考点的信息，包括每个参考点的期望位置xd，yd、期望航向角θd，期望输入δd，k_d是下标索引值
function [xd,yd,thetad,deltad,k_d] = fcn(Xr,Yr,k)
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l=1.923;
e_min=1;
k_d=k;
kend=k+50;
if kend>=length(px)
kend=length(px);
end
for i=k:kend
e=sqrt((Xr-px(i))^2+(Yr-py(i))^2);
if e<e_min
e_min=e;
k_d=i;
end
end
xd=px(k_d);
yd=py(k_d);
thetad=ptheta(k_d);
deltad=PR(k_d);
然后是MPC控制器，输入为自车信息，参考点的4个信息，以及期望速度（-2.88/3.6=-0.8m/s），不去控制速度，直接输出，同时对计算出的前轮转角做一个最后回正的switch判断。规划出的泊车路径如下，总共有648个离散轨迹点
MPC具体的代码如下
首先定义初始参数
tstart=tic;%开始计时
Nx=3;%状态量的个数（X，Y，Yaw）
Nu=2;%控制量的个数（v，delta）
Np=30;%预测步长Np
Nc=1;%控制步长Nc
Row=10;%松弛因子
t_d =Yaw*pi/180;%角度转弧度
r=zeros(3,1);%目标点位置，姿态向量
r(1)=xd;
r(2)=yd;
r(3)=thetad;
vd1=V;%参考速度、转角
vd2=deltad;
预测时域Np=30，控制时域Nc=1，松弛因子row=10，当然这个是人为设置的，然后是系统运动学方程的3个状态变量和两个输入
然后是我们构建的新的状态空间的变量
kesi=zeros(Nx+Nu,1);%状态向量，控制向量
kesi(1)=Xr-r(1);%Xr==X(1)
kesi(2)=Yr-r(2);%Yr==X(2)
kesi(3)=t_d-r(3);%Yaw==X(3)
kesi(4)=U1;
kesi(5)=U2;
T=0.02;%仿真步长
L=1.923;%车辆轴距
然后是系统的状态矩阵
a=[1 0 -vd1*sin(t_d)*T;
0 1 vd1*cos(t_d)*T;
0 0 1];
b=[cos(t_d)*T 0;
sin(t_d)*T 0;
tan(vd2)*T/L vd1*T/(cos(vd2)^2)];
A=[a,b;
zeros(Nu,Nx),eye(Nu)];
B=[b;eye(Nu)];
C=[1 0 0 0 0;
0 1 0 0 0;
0 0 1 0 0;];
a和b是线性离散化之后的车辆运动学模型，A和B和C是构建的新的状态空间方程的状态矩阵，输入矩阵和输出矩阵
然后是是预测，就是将优化问题转化为QP问题
PHI=[];
THETA=[];
for j=1:1:Np
PHI=[PHI;C*A^j];
for k=1:1:Nc
THETA=[THETA;C*A^(j-k)*B];
end
end
error=PHI*kesi;
Q=100*eye(Nx*Np,Nx*Np);
R=5*eye(Nu*Nc);
H=[THETA'*Q*THETA+R,zeros(Nu*Nc,1);
zeros(1,Nu*Nc),Row];
f=[2*error'*Q*THETA,0];
PHI和THETA分别是输出方程的系数，error就是E，Q,R是人为调试的参数，H和f就是QP问题中的参数
然后是处理约束
%% 约束
%不等式约束
A_t=zeros(Nc,Nc);
for p=1:1:Nc
for q=1:1:Nc
if q<=p
A_t(p,q)=1;
else
A_t(p,q)=0;
end
end
end
A_I=kron(A_t,eye(Nu));
Ut=kron(ones(Nc,1),[U1;U2]);
umin=[-0.1;-0.08];
umax=[0.1;0.08];
delta_umin=[-0.02;-0.002;];
delta_umax=[0.02;0.002];
Umin=kron(ones(Nc,1),umin);
Umax=kron(ones(Nc,1),umax);
A_cons=[A_I zeros(Nu*Nc,1);-A_I zeros(Nu*Nc,1)];
b_cons=[Umax-Ut;-Umin+Ut];
%状态量约束
M=10;
delta_Umin=kron(ones(Nc,1),delta_umin);
delta_Umax=kron(ones(Nc,1),delta_umax);
lb=[delta_Umin;0];
ub=[delta_Umax;M];
使用matlab自带的quadprog求解器求解QP问题
%% 求解二次规划问题
opts = optimoptions('quadprog','Algorithm','active-set');
x_start=zeros(2*Nc+1,1);
[X,fval,exitflag]=quadprog(H,f,A_cons,b_cons,[],[],lb,ub,x_start,opts);
最后得到输出
%% 计算输出
U1_=kesi(4)+X(1);%上一时刻输出误差加优化结果得到本时刻误差
U2_=kesi(5)+X(2);
v_des=U1_+vd1;%误差加参考值得到最终输出值
delta_des=U2_+vd2;
delta_t=toc(tstart);%计时结束
最后的输出=上一时刻的偏差【U（k-1）-Ur（k-1）】+优化求得的结果ΔU+参考值Ur（k）
三、仿真验证
车辆初始位置：SV_XO 9.423；SV_YO 1.1
仿真时间设置为15s，仿真效果如下，贴个视频(背景音乐有点吵)
关于参数的说明
Np越大，系统的稳定性越高，响应速度慢；Nc越大，控制灵敏度越高，但系统的稳定性和鲁棒性会下降；Q和R矩阵越大，说明误差状态量和输入的约束权重越大。
参数的调整是很重要的，需要更多的经验来体会！
标签：控制,轨迹,MPC,输出,模型,Nc,矩阵,delta,泊车 From： https://www.cnblogs.com/autodriver/p/18099488