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MPC_errdyn_fmincon_pid.m
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MPC_errdyn_fmincon_pid.m
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clear all
close all
clc
%%
ticid=tic;
%%
load('TestTrack.mat')
bl_x = TestTrack.bl(1,:);
bl_y = TestTrack.bl(2,:);
br_x = TestTrack.br(1,:);
br_y = TestTrack.br(2,:);
cline_x = TestTrack.cline(1,:);
cline_y = TestTrack.cline(2,:);
theta = TestTrack.theta(1,:);
%% Obstacle generation
rng(42)
nobs=10;%no of obstacles
Xobs = generateRandomObstacles(nobs,TestTrack);
%% Reference input
% load('Control_Nom.mat')
% load('Trajectory_Nom.mat')
% u_ol=Control_Nom;
% x_ol=Y;
% clear Control_Nom Y
% load('traj_cline_slow.mat')
load('traj_cline_fast.mat')
%% Run open loop to get equilibrium trajectory
% x0 = [287 5 -176 0 2 0]';
% x_ol = forwardIntegrateControlInput(u_ol,x0);
t_sim=0:0.01:(size(u_ol,1)-1)*0.01;
%% Collision detection
h1=figure;
hold on
plot(bl_x,bl_y,br_x,br_y,'b');%cline_x,cline_y,'--',
for pp=1:nobs
obs_x_mat(:,pp)=Xobs{pp}(1:4,1);
obs_y_mat(:,pp)=Xobs{pp}(1:4,2);
obs_c_mat(:,pp)=[mean(Xobs{pp}(1:4,1));mean(Xobs{pp}(1:4,2))];
obs_fm_mat(:,pp)=[mean(Xobs{pp}(1:2,1));mean(Xobs{pp}(1:2,2))];
obs_bm_mat(:,pp)=[mean(Xobs{pp}(3:4,1));mean(Xobs{pp}(3:4,2))];
th_obs(pp)=atan2(obs_bm_mat(2,pp)- obs_fm_mat(2,pp),obs_bm_mat(1,pp)- obs_fm_mat(1,pp));
%Closest points on trajectory to obstacle edges
for mmm=1:4
[mindist,indcl]=sort(((obs_x_mat(mmm,pp)-x_ol(:,1)).^2+(obs_y_mat(mmm,pp)-x_ol(:,3)).^2),'ascend');
cl_obs_traj(mmm,:)=[x_ol(indcl(1),1) x_ol(indcl(1),3)];
end
[in,on] = inpolygon(cl_obs_traj(:,1),cl_obs_traj(:,2),obs_x_mat(:,pp),obs_y_mat(:,pp));
if (numel(cl_obs_traj(in,1))>0 || numel(cl_obs_traj(on,1))>0)
avoid_obs(pp)=1;
else
avoid_obs(pp)=0;
end
plot(Xobs{pp}(1:2,1),Xobs{pp}(1:2,2),'-r')
plot(Xobs{pp}(2:3,1),Xobs{pp}(2:3,2),'-g')
plot(Xobs{pp}(3:4,1),Xobs{pp}(3:4,2),'-b')
plot(Xobs{pp}([4 1],1),Xobs{pp}([4 1],2),'-m')
plot(obs_fm_mat(1,pp),obs_fm_mat(2,pp),'v')
plot(obs_bm_mat(1,pp),obs_bm_mat(2,pp),'v')
end
%% Initialization
detect_dist=10;%Detection distance of obstacle in m
lim_pdist=1.5;%Limiting distance on side of obstacleto considet going to far side
dt = 0.1;
t=0:dt:20;%floor(t_sim(end)/dt)*dt;%500*dt;%117.5;
x_ref=interp1(t_sim,x_ol,t,'previous');
u_ref=interp1(t_sim,u_ol,t,'previous');%);%
figure(h1);
hold on
plot(x_ref(:,1),x_ref(:,3),'-.m');
x = x_ref(1,:)';% [289 5 -175 0 2 0]';%[287 5 -176 0 2 0]';
e = x-x_ref(1,:)';
u = u_ref(1,:);%[-0.02 5000];
%PID
u_pid=[0 0];%Initial pid inputs
K_p=zeros(2,6);%Initial pid inputs
int_err=zeros(3,1);%Initial integral errors
%Normal parameters
err_UB=[100 20 100 20 1 1];%Error upper bounds
err_LB=-err_UB;%Error lower bounds
u_lim=[-0.2 0.2;-1000 5000];%Total input bounds
obs_pad=1;%Obstacle padding in m
tracklim_pad=3;%Track limit padding in m
t_comp_mins=15;%No. of simulation minutes allowed for competition
n = 6; %Number of States
m = 2; %Number of Inputs
horizon=2;
horizon_def=horizon;
zsize = (horizon+1)*n+horizon*m;
xsize = (horizon+1)*n;
Q = diag(repmat((ones(1,n)./[100 10 100 10 0.1 0.01]).^2,1,horizon+1));%eye(xsize);%
R = zeros(zsize-xsize);%diag(repmat([1 1]./[0.01 1000],1,horizon));%
H = blkdiag(Q, R);
f = zeros(zsize, 1);
Ndec=n * (horizon+1) + m *horizon ;
options=optimoptions(@fmincon,'Algorithm','sqp','SpecifyObjectiveGradient',true);%'SpecifyConstraintGradient',true);%'CheckGradients',true);%,'Display','Iter');%,'CheckGradients',false,'GradObj','on','ConstraintTolerance',1e-6,'MaxIter',10000,'MaxFunctionEvaluations',50000,'Display','Iter');%,[repmat([100;5;100;5;1;0.1],(horizon+1),1) ; repmat([0.1;1000],horizon,1)]'TypicalX',sqrt(1./diag(Q))
go_left_mat=[];
exitflag_mat=[];
recover_norm=1e-6;
MPC_flag=0;
k=0;
pp=[];
exit_flag=0;
e_nl_mat=[];
%MPC at every time step k
while exit_flag==0
k=k+1;
% ((norm(x(:,k)-x_ref(k,:)')/norm(x_ref(k,:)'))<recover_norm)
% MPC_flag
if ((k==(length(t)-1)) || toc(ticid)>=0.95*t_comp_mins*60) %
exit_flag=1;
end
disp(['At ' num2str(t(k)) ' s:'])
%Detect how many obstacles are close at initial time
[d2_det,ind_det]=find(((x(1,k)- obs_fm_mat(1,:)).^2+(x(3,k)- obs_fm_mat(2,:)).^2)<detect_dist^2);%Index of obstacles which are close enough
n_det=length(ind_det); %No of closest obstacles detected
if (n_det>0) %If obstacle is detected change horizon
%Index of closest obstacle
[mind2_obs,pp]=min((x(1,k)- obs_fm_mat(1,:)).^2+(x(3,k)- obs_fm_mat(2,:)).^2);
%Vehicle and obstacle points in obstacle C.S
R_obs=[cos(th_obs(pp)) sin(th_obs(pp));-sin(th_obs(pp)) cos(th_obs(pp))];%Rotation matrix to get from world CS to obstacle CS
xvl=R_obs*[x(1,k);x(3,k)];
xobl=R_obs*[obs_x_mat(:,pp)';obs_y_mat(:,pp)'];
%Change horizon only id closest obstacle is in front and it clashes
%with trajectory
if (xvl(1)<=max(xobl(1,:)) && (avoid_obs(pp)==1) && (MPC_flag==0))
MPC_flag=1;
horizon = 3;%ceil(detect_dist/(dt*norm(mean(x_ref(:,[2 4])))));%No of steps to reach obstacle approximately
elseif (xvl(1)>max(xobl(1,:)) && (avoid_obs(pp)==1))
horizon=horizon_def;
if (((norm(x(:,k)-x_ref(k,:)')/norm(x_ref(k,:)'))<recover_norm) && (MPC_flag==1))
MPC_flag=0;
end
end
else
horizon=horizon_def;
if (((norm(x(:,k)-x_ref(k,:)')/norm(x_ref(k,:)'))<recover_norm) && (MPC_flag==1))
MPC_flag=0;
end
end
%Evaluate Al and Bl at current state
[Al,Bl]=linearized_mats(x_ref(k,:),u_ref(k,:));
sysc=ss(Al,Bl,[],[]);
%Discretize (Euler)
% A = eye(size(Al))+dt*Al;
% B = dt*Bl;
sysd=c2d(sysc,dt,'zoh');
A=sysd.A;
B=sysd.B;
if (MPC_flag==1)
if ~(length(t)-k>horizon)
horizon = length(t)-k;
end
zsize = (horizon+1)*n+horizon*m;
xsize = (horizon+1)*n;
Q = diag(repmat((ones(1,n)./[100 10 100 10 0.1 0.01]).^2,1,horizon+1));%1000*eye(xsize);
R = zeros(zsize-xsize);
H = blkdiag(Q, R);
f = zeros(zsize, 1);
Ndec=n * (horizon+1) + m *horizon ;
fun=@(var)error_cost(var,H,f);
% nlcons=@(var)track_nlcons_err(var,reshape(x_ref(k:k+horizon,:)',n*(horizon+1),1),TestTrack,Ndec,horizon,n,m);%Nonlinear track constraints
%% Generate equality constraints
Aeq = zeros(xsize, zsize); %Allocate Aeq
Aeq(1:n, 1:n) = eye(n); %Initial Condition LHS
beq = zeros(xsize, 1); %Allocate beq
beq(1:n) = e(:,end); %Initial Condition RHS
j = xsize+1;
for i = n+1:n:xsize
Aeq(i:i+n-1, i:i+n-1) = -eye(n); %x(k+1) term
Aeq(i:i+n-1, i-n:i-1) = A; %A*x(k) term
Aeq(i:i+n-1, j:j+m-1) = B; %B*u(k) term
j = j+m;
end
%
%% Inequality constraints
%Inequality constraints for input limits
%Upper Bounds
Aineq = zeros(2*Ndec, Ndec);
bineq = zeros(2*Ndec, 1);
Aineq(1:n * (horizon+1),1:n * (horizon+1))=eye(n * (horizon+1));
bineq(1:n * (horizon+1),1)=repmat(err_UB',(horizon+1),1);
Aineq(n * (horizon+1)+[1:m*horizon],n * (horizon+1)+[1:m*horizon])=eye(m*horizon);
bineq(n * (horizon+1)+[1:2:m*horizon],1)=u_lim(1,2)-u_ref(k:k+horizon-1,1)-u_pid(1);
bineq(n * (horizon+1)+[2:2:m*horizon],1)=u_lim(2,2)-u_ref(k:k+horizon-1,2)-u_pid(2);
%Lower Bounds
Aineq(Ndec+[1:n * (horizon+1)],1:n * (horizon+1))=-eye(n * (horizon+1));
bineq(Ndec+[1:n * (horizon+1)],1)=repmat(-err_LB',(horizon+1),1);
Aineq(Ndec+n * (horizon+1)+[1:m*horizon],n * (horizon+1)+[1:m*horizon])=-eye(m*horizon);
bineq(Ndec+n * (horizon+1)+[1:2:m*horizon],1)=u_lim(1,2)-u_ref(k:k+horizon-1,1)-u_pid(1);
bineq(Ndec+n * (horizon+1)+[2:2:m*horizon],1)=u_lim(2,2)-u_ref(k:k+horizon-1,2)-u_pid(2);
%Inequality constraints for slip angle limits
Asl = zeros(4*horizon, Ndec);
bsl = zeros(4*horizon,1);
for lll=1:horizon
Asl(lll,lll*n+[2 4 6])=[-0.3 1 1.35];
Asl(horizon+lll,lll*n+[2 4 6])=[-0.3 -1 -1.35 ];
Asl(2*horizon+lll,lll*n+[2 4 6])=[-0.05 1 -1.45];
Asl(3*horizon+lll,lll*n+[2 4 6])=[-0.05 -1 1.45 ];
end
Aineq= [Aineq; Asl];
bineq= [bineq;bsl];
%Inequality constraints for input limits
% Aineq = zeros( 2*m*horizon, Ndec);
% bineq = zeros( 2*m*horizon, 1 );
% Aineq(1:m*horizon,n * (horizon+1)+[1:m*horizon])=eye(m*horizon);
% bineq([1:2:m*horizon],1)=u_lim(1,2)-u_ref(k:k+horizon-1,1);
% bineq([2:2:m*horizon],1)=u_lim(2,2)-u_ref(k:k+horizon-1,2);
% Aineq(m*horizon+[1:m*horizon],n * (horizon+1)+[1:m*horizon])=-eye(m*horizon);
% bineq(m*horizon+[1:2:m*horizon],1)=-u_lim(1,1)+u_ref(k:k+horizon-1,1);
% bineq(m*horizon+[2:2:m*horizon],1)=-u_lim(2,1)+u_ref(k:k+horizon-1,2);
%
%Inequality constraints for obstacles
if n_det>0
figure(h1)
plot(x(1,k),x(3,k),'sr')
%Index of closest obstacle
% [mind2_obs,pp]=min((x(1,k)- obs_fm_mat(1,:)).^2+(x(3,k)- obs_fm_mat(2,:)).^2);
%
% % for pp=1:n_det %Loop over obstacles wthin range
% %Vehicle and obstacle points in obstacle C.S
% R_obs=[cos(th_obs(pp)) sin(th_obs(pp));-sin(th_obs(pp)) cos(th_obs(pp))];;%Rotation matrix to get from world CS to obstacle CS
% xvl=R_obs*[x(1,k);x(3,k)];
% xobl=R_obs*[obs_x_mat(:,pp)';obs_y_mat(:,pp)'];
% h2=figure
% plot(xvl(1),xvl(2),'s')
% hold on
% plot(xobl(1,:),xobl(2,:),'-')
% % keyboard
if (xvl(1)<max(xobl(1,:)))
%Perpendicular distances from left side of obstacle to left side of track
pdist1=perp_dist(obs_x_mat(1,pp),obs_y_mat(1,pp),TestTrack.bl);
pdist4=perp_dist(obs_x_mat(4,pp),obs_y_mat(4,pp),TestTrack.bl);
pdistl=min(pdist1,pdist4);
%Perpendicular distances from right side of obstacle to right side of track
pdist2=perp_dist(obs_x_mat(2,pp),obs_y_mat(2,pp),TestTrack.br);
pdist3=perp_dist(obs_x_mat(3,pp),obs_y_mat(3,pp),TestTrack.br);
pdistr=min(pdist2,pdist3);
%Which side of obstacle is closest to car
[mind2,indmin]=min((x(1,k)- obs_x_mat(1:2,pp)).^2+(x(3,k)- obs_y_mat(1:2,pp)).^2);
if (indmin==1 && pdistl>lim_pdist) %left side is closer and there is space to go on that side
goleft=1;
elseif (indmin==2 && pdistr>lim_pdist) %right side is closer and there is space to go on that side
goleft=0;
else
if (pdistl>pdistr)%More space on left side even if its lower than limit
goleft=1;
else %More space on left side even if its lower than limit
goleft=0;
end
end
go_left_mat=[go_left_mat goleft];
%Get slopes and y-intercepts of constraining lines
if (goleft==1) %Going left
if ( xvl(1)<xobl(1,1)) %if car is approximately in front of obstacle
m_cons=((xobl(2,1)+obs_pad)-xvl(2))/(xobl(1,1)-xvl(1));
c_cons=xvl(2)+obs_pad-m_cons*xvl(1);
flag_cons=1;
elseif (xvl(1)>xobl(1,1) && xvl(1)<xobl(1,4)) %if car is approximately to the side of obstacle
m_cons=(xobl(2,4)-xobl(2,1))/(xobl(1,4)-xobl(1,1));
c_cons=xobl(2,1)+obs_pad-m_cons*xobl(1,1);
flag_cons=1;
else
flag_cons=0;
end
else %Going right
if( xvl(1)<xobl(1,2)) %if car is approximately in front of obstacle
m_cons=(xobl(2,2)-obs_pad-xvl(2))/(xobl(1,2)-xvl(1));
c_cons=xvl(2)-obs_pad-m_cons*xvl(1);
flag_cons=1;
elseif (xvl(1)>xobl(1,2) && xvl(1)<xobl(1,3)) %if car is approximately to the side of obstacle
m_cons=(xobl(2,3)-xobl(2,2))/(xobl(1,3)-xobl(1,2));
c_cons=xobl(2,2)-obs_pad-m_cons*xobl(1,2);
flag_cons=1;
else
flag_cons=0;
end
end
if (flag_cons==1)
Aex=zeros(1,Ndec);%zeros(horizon,Ndec);
bex=zeros(1,1);
if (goleft==1)
%Extra constraint equations for going left
Aex(1, horizon*n+[1 3])=[m_cons -1]*R_obs;
bex(1,1)=-c_cons-[m_cons -1]*R_obs*[x_ref(k+horizon,1);x_ref(k+horizon,3)];
% for lll=1:horizon
% Aex(lll, lll*n+[1 3])=[m_cons -1]*R_obs;
% bex(lll,1)=-c_cons-[m_cons -1]*R_obs*[x_ref(k+lll,1);x_ref(k+lll,3)];
% end
else
%Extra constraint equations for going right
Aex(1, horizon*n+[1 3])=-[m_cons -1]*R_obs;
bex(1,1)=c_cons+[m_cons -1]*R_obs*[x_ref(k+horizon,1);x_ref(k+horizon,3)];
% for lll=1:horizon
% Aex(lll, lll*n+[1 3])=-[m_cons -1]*R_obs;
% bex(lll,1)=c_cons+[m_cons -1]*R_obs*[x_ref(k+lll,1); x_ref(k+lll,3)];
% end
end
Aineq=[Aineq;Aex];
bineq=[bineq;bex];
end
flag_detect=1;
else
flag_detect=0;
end
% end
end
%Inequality constraints (approximate linear) for track
%Find closest points on track
[mindist_tr,ind_cl_tr]=sort(((x(1,k)-TestTrack.cline(1,:)).^2+(x(3,k)-TestTrack.cline(2,:)).^2),'ascend');
ind_tr=[min(ind_cl_tr(1:2)) max(ind_cl_tr(1:2))];
%Locations of closest centerline points
xc1=TestTrack.cline(1,ind_tr(1)); yc1=TestTrack.cline(2,ind_tr(1));
xc2=TestTrack.cline(1,ind_tr(2)); yc2=TestTrack.cline(2,ind_tr(2));
th_tr=atan2((yc2-yc1),(xc2-xc1));%Slope of centerline segment
R_tr=[cos(th_tr) sin(th_tr);-sin(th_tr) cos(th_tr)];%Rotation matrix to get from world CS to track segment CS
%Locations of closest left track points
xl1=TestTrack.bl(1,ind_tr(1)); yl1=TestTrack.bl(2,ind_tr(1));
xl2=TestTrack.bl(1,ind_tr(2)); yl2=TestTrack.bl(2,ind_tr(2));
%Locations of closest right track points
xr1=TestTrack.br(1,ind_tr(1)); yr1=TestTrack.br(2,ind_tr(1));
xr2=TestTrack.br(1,ind_tr(2)); yr2=TestTrack.br(2,ind_tr(2));
%Express in track C.S
xc_tr=R_tr*[xc1 xc2;yc1 yc2];
xr_tr=R_tr*[xr1 xr2;yr1 yr2];
xl_tr=R_tr*[xl1 xl2;yl1 yl2];
xv_tr=R_tr*[x(1,k);x(3,k)];
Aex2=zeros(2*horizon,Ndec);
bex2=zeros(2*horizon,1);
for lll=1:horizon
Aex2(lll,lll*n+[1 3])=R_tr(2,:);%y-error for vehicle in track C.S
Aex2(horizon+lll,lll*n+[1 3])=-R_tr(2,:);%negative y-error for vehicle in track C.S
bex2(lll,1)=min(xl_tr(2,:))-tracklim_pad-R_tr(2,:)*[x_ref(k+lll,1);x_ref(k+lll,3)];%closest point of track on left side in in track C.S
bex2(horizon+lll,1)=-(max(xr_tr(2,:))+tracklim_pad)+R_tr(2,:)*[x_ref(k+lll,1);x_ref(k+lll,3)];%closest point of track on right side in in track C.S
end
Aineq=[Aineq;Aex2];
bineq=[bineq;bex2];
% figure(3)
% hold on
% plot(R_tr(1,:)*[x(1,k);x(3,k)],R_tr(2,:)*[x(1,k);x(3,k)],'s')
% plot(xc_tr(1,:),xc_tr(2,:),'g')
% plot(xr_tr(1,:),xr_tr(2,:),'b')
% plot(xl_tr(1,:),xl_tr(2,:),'--k')
% plot(xl_tr(1,:),( bex2(1,1))*ones(2,1),'--m')
% plot(xr_tr(1,:),-(bex2(horizon+1,1))*ones(2,1),'--m')
% keyboard
% Inequality constraints to make sure car is moving forward
Aex3=zeros(horizon,Ndec);
bex3=zeros(horizon,1);
for lll=1:horizon
Aex3(lll,(lll-1)*n+[1 3])=R_tr(1,:);%x-error for vehicle in track C.S at lll-1=k:k+horizon-1
Aex3(lll,lll*n+[1 3])=R_tr(1,:);%x-error for vehicle in track C.S at lll=k:k+horizon
bex3(lll,1)=R_tr(1,:)*([x_ref(k+lll,1);x_ref(k+lll,3)]-[x_ref(k+lll-1,1);x_ref(k+lll-1,3)]);%Difference between references in track C.S
end
Aineq=[Aineq;Aex3];
bineq=[bineq;bex3];
%% Simulate open loop for initial guess
z0=zeros(Ndec,1);
e_curr=x(:,k)-x_ref(k,:)';
z0([1:n])=e_curr;
for ll=2:horizon+1
e_curr=A*e_curr;%+B*u(k,:)';
z0((ll-1)*n+[1:n])=e_curr;
end
%% Minimize
% z0(n*(horizon+1)+[1:m*horizon])=repmat(u(k,:)',1,horizon);
[z,fcostval,exitflag,output] = fmincon(fun,z0,Aineq,bineq,Aeq,beq,[],[],[],options);
exitflag_mat=[exitflag_mat exitflag];
u_k = z([xsize+1 xsize+2],1);
u(k+1,:) = u_k'+u_ref(k,:)+u_pid; %augmenting with nominal input
e= [e z([n+1:2*n],1)];
x=[x x_ref(k+1,:)'+z([n+1:2*n],1)]; %updating states
% Simulating using the total input
% [L]=forwardIntegrate_vardt([u(k:k+1,:)],x(:,k)',dt);
% df = state_transition_euler(x(:,k),u(k,:));
% xnew=x(:,k)+dt*df;
% x=[x xnew]; %updating states
% e= [e xnew-x_ref(k+1,:)'];
else %%When states are close to 0 error and openloop can be run
%Find closest points on track
[mindist_tr,ind_cl_tr]=sort(((x(1,k)-TestTrack.cline(1,:)).^2+(x(3,k)-TestTrack.cline(2,:)).^2),'ascend');
ind_tr=[min(ind_cl_tr(1:2)) max(ind_cl_tr(1:2))];
%Locations of closest centerline points
xc1=TestTrack.cline(1,ind_tr(1)); yc1=TestTrack.cline(2,ind_tr(1));
xc2=TestTrack.cline(1,ind_tr(2)); yc2=TestTrack.cline(2,ind_tr(2));
th_tr=atan2((yc2-yc1),(xc2-xc1));%Slope of centerline segment
R_tr=[cos(th_tr) sin(th_tr);-sin(th_tr) cos(th_tr)];%Rotation matrix to get from world CS to track segment CS
u(k+1,:) = u_ref(k,:)+u_pid; %augmenting with nominal input
if u(k+1,1)>u_lim(1,2)
u(k+1,1)=u_lim(1,2);
elseif u(k+1,1)<u_lim(1,1)
u(k+1,1)=u_lim(1,1);
end
if u(k+1,2)>u_lim(2,2)
u(k+1,2)=u_lim(2,2);
elseif u(k+1,2)<u_lim(2,1)
u(k+1,2)=u_lim(2,1);
end
%
e_new=A*e(:,end)+B*(u(k+1,:)'-u_ref(k+1,:)');
e= [e e_new];
x=[x x_ref(k+1,:)'+e(:,end)]; %updating states
% e= [e zeros(n,1)];
% x=[x x_ref(k+1,:)']; %updating states
end
%%
figure(h1)
hold on
plot([x(1,k) x(1,k+1)],[x(3,k) x(3,k+1)],'--g')
if rem(k,10)==0%Every few steps check error for ode45 drifts and add extra control
u_chk=interp1(t(1:k+1),u(1:k+1,:),t_sim(t_sim<=t(k+1)),'previous');%,[],0);
x_chk = forwardIntegrateControlInput(u_chk,x(:,1));
% x_chk=interp1(t_sim(t_sim<=t(k+1)),x_chk,t(1:k+1));
x_chk =x_chk';
K_p=zeros(2,6);%%PID gains
K_p(1,[1 3 5])=[-0.005*R_tr(2,1) -0.005*R_tr(2,2) -1*0.005];%Steering P gains for local y-error( rows 1 and 2) and psi error (row 3)
int_err(1:2)=int_err(1:2)+R_tr*[x_chk([1 3],end)-x([1 3],end)];%Steering I error for local y-error
int_err(3)=int_err(3)+[x_chk(5,end)-x(5,end)];%Steering I error for psi error
K_p(1,[1 3])=[-0.1*R_tr(1,1) -0.1*R_tr(1,2)]; %Throttle P gains for local y-error( rows 1 and 2)
u_pid=K_p*[x_chk(:,end)-x(:,end)]+[-0.001*int_err(2)-0.0005*int_err(3);-0.01*int_err(1)];
u_pid=u_pid';
e_nl_mat=[e_nl_mat x_chk(:,end)-x(:,end)];
if u_pid(1)>0.1
u_pid(1)=0.1;
elseif u_pid(1)<-0.1
u_pid(1)=-0.1;
end
if u_pid(2)>2000
u_pid(2)=2000;
elseif u_pid(2)<-2000
u_pid(2)=-2000;
end
figure(h1);
plot(x_chk(1,end),x_chk(3,end),'pr');
plot(x_chk(1,:),x_chk(3,:),'pr');
end
% if exist('flag_cons')
% if (flag_cons==1)
% if max(Aex*z-bex)>0
% keyboard
% end
% end
% end
flag_cons=[];
Aex=[];
bex=[];
Aex2=[];
bex2=[];
end
%%
figure;
plot(t(1:k+1),e(:,1:k+1))
legend('e_x','e_u','e_y','e_v','e_{\psi}','e_{r}')
%% Re-interpolate final input vector
u_final=interp1(t(1:k+1),u(1:k+1,:),t_sim(t_sim<=t(k+1)),[],0);
u_final=[u_final;u_ol(t_sim>t(k+1),:)];
calc_time=toc(ticid)
figure;
subplot(211)
hold on
plot(u_ol(:,1));plot(u_final(:,1),'--r')
subplot(212)
hold on
plot(u_ol(:,2));plot(u_final(:,2),'--r')
%% Re-calculate trajectory with new input
% ticid_sim=tic;
% x_final = forwardIntegrateControlInput(u_final,x(:,1));
% figure(h1);
% plot(x_final(:,1),x_final(:,3),'--k');
% sim_time=toc(ticid_sim)
% save('quadpog_obstacle_avoid_40s.mat')