mirror of
https://github.com/ROB-535-F21-Team-3/Control-Project.git
synced 2025-08-19 17:22:45 +00:00
Incorporate xenia-nonlinear branch changes
- Copy-paste `xenia_nonlinearopt.m` from xenia-nonlinear branch
This commit is contained in:
Sravan Balaji
@@ -1,3 +1,4 @@
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%% initial conditions
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%Vehicle Parameterrs
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Nw=2;
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f=0.01;
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@@ -30,131 +31,126 @@ curr_pos = [init(1);init(3)];
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% [g,h,dg,dh]=nonlcon(z, Xobs, nsteps);
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% [J, dJ] = costfun(z, TestTrack.cline(:,1), TestTrack.theta(1), nsteps);
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%%okay, idea is to create an iterative process that uses the centerline as a
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%checkpoint
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%keep trajecting until closer to the next point on centerline, then repeat
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%bounds will be based on index of current&previous cp index
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%cost function uses next checkpoint in centerline
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%generate z and make it bigger if didn't reach close enough to the goal
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%(modify nsteps)
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Nobs = randi([10 25], 1,1);
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global Xobs
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Xobs = generateRandomObstacles(Nobs);
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% global index_cl_cp %index of current checkpoint in centerline
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% index_cl_cp = 1;
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% global index_cl_nextcp %index of next checkpoint in centerline
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% index_cl_nextcp = 2;
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%
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% global nsteps
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% nsteps = 100;
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% x0 = init;
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% x0vec = [];
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% %initialize x0vec to be initial condition for n steps and Sravan's ref inputs
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% %note: function 'initvec' changes inputs, but i'm not sure how
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% %initialization should look with fmincon
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% for i = 1:nsteps
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% x0vec = [x0vec, init];
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% end
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% for i = 1:nsteps-1
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% x0vec = [x0vec, -0.004, 3900];
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% end
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% dist12atcp = [];
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% iter = 0;
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%
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% factor = 0.1; %next checkpoint if significantly closer to next checkpoint
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U_final = [];
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Y_final = [];
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options = optimoptions('fmincon','SpecifyConstraintGradient',true,...
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'SpecifyObjectiveGradient',true) ;
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load('ROB535_ControlProject_part1_Team3.mat');
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%[Y_submission, T_submission] = forwardIntegrateControlInput(ROB535_ControlProject_part1_input, init);
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load('reftrack_info.mat');
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load('segments_info.mat');
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for i = 1:length(num_pts)
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[start_idx, end_idx] = get_indices(i, num_pts);
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%% MPC
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U_ref = ROB535_ControlProject_part1_input';
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Y_ref = Y_submission';
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dt = 0.01;
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delta = delta_vals(i);
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F_x = F_x_vals(i);
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%discretize dynamics
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F_zf=b/(a+b)*m*g;
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F_zr=a/(a+b)*m*g;
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if (end_idx >= size(Y_submission,1))
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start_idx = start_idx - 1;
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end_idx = end_idx - 1;
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end
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%we are just going to use cornering stiffness to make linear so this derivative
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%easier, the vehicle parameter's are close enough to problem 1 hw 2
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B=10;
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C=1.3;
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D=1;
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Ca_r= F_zr*B*C*D;
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Ca_f= F_zf*B*C*D;
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x0 = [];
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for j = start_idx:end_idx+1 %+1 end idx to keep z size consistent to hw
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x0 = [x0, Y_submission(j,:)];
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end
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x = @(s,i) Y_ref(s,i);
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for j = start_idx:end_idx
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x0 = [x0, delta, F_x];
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end
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Ac = @(i) [0, cos(x(5,i)), 0, -sin(x(5,i)), x(2,i)*sin(x(5,i))-x(4,i)*cos(x(5,i)), 0;
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0, (-1/m)*Ca_f*x(2,i)^-2, 0, -Ca_f/m + 1, 0, Ca_f*(-a/m) + 1;
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0, sin(x(5,i)), 0, cos(x(5,i)), -x(4,i)*sin(x(5,i))+x(2,i)*cos(x(5,i)), 0;
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0, (1/m)*(-Ca_f*x(2,i)^-2 - Ca_r*x(2,i)^-2) - 1, 0, Ca_r/m*(-1/x(2,i)) + Ca_f/m*(-1/x(2,i)), 0, Ca_r/m*(b/x(2,i)) + Ca_f/m*(-a/x(2,i)) - x(2,i);
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0, 0, 0, 0, 0, 1
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0, (1/Iz)*(-Ca_f*a*x(2,i)^-2 - b*Ca_r*x(2,i)^-2), 0, -b*Ca_r/Iz*(-1/x(2,i)) + a*Ca_f/Iz*(-1/x(2,i)), 0, -b*Ca_r/Iz*(b/x(2,i)) + a*Ca_f/Iz*(-a/x(2,i))];
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%start and end index used to maintain size of vectors
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[lb, ub] = bounds(start_idx, end_idx);
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Bc = @(i) [0, -Ca_f/m, 0, Ca_f/m, 0, a*Ca_f/Iz;
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0, Nw/m, 0, 0, 0, 0]';
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%define for cost function, goal is to reach end of segment
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global target_vec
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target_vec = [Y_submission(end_idx,1), Y_submission(end_idx,3), Y_submission(end_idx,5)];
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A = @(i) eye(6) + dt*Ac(i);
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B = @(i) dt*Bc(i);
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global nsteps
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nsteps = num_pts(i)+1;
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cf=@costfun
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nc=@nonlcon
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z=fmincon(cf,x0,[],[],[],[],lb',ub',nc,options);
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Y0=reshape(z(1:6*nsteps),6,nsteps)';
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U=reshape(z(6*nsteps+1:end),2,nsteps-1)';
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info = getTrajectoryInfo(Y0,U)
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U_final = [U_final; U];
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%Decision variables
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npred = 10;
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nstates = 6;
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ninputs = 2;
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Ndec=(npred+1)*nstates+ninputs*npred;
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input_range = [-0.5, 0.5; -5000, 5000];
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eY0 = init';
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[Aeq_test1,beq_test1]=eq_cons(1,A,B,eY0,npred,nstates,ninputs);
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%simulate forward
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T = 0:0.01:(size(Y_ref,2)/100);
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%final trajectory
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Y=NaN(nstates,length(T));
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%applied inputs
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U=NaN(ninputs,length(T));
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%input from QP
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u_mpc=NaN(ninputs,length(T));
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%error in states (actual-reference)
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eY=NaN(nstates,length(T));
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%set random initial condition
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Y(:,1)=eY0+Y_ref(:,1);
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for i=1:length(T)-1
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i
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%shorten prediction horizon if we are at the end of trajectory
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npred_i=min([npred,length(T)-i]);
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%calculate error
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eY(:,i)=Y(:,i)-Y_ref(:,i);
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%generate equality constraints
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[Aeq,beq]=eq_cons(i,A,B,eY(:,i),npred_i,nstates,ninputs);
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%generate boundary constraints
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[Lb,Ub]=bound_cons(i,U_ref,npred_i,input_range,nstates,ninputs);
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%cost for states
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Q=[1,1,1,1,1,1];
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%cost for inputs
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R=[0.1,0.01];
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H=diag([repmat(Q,[1,npred_i+1]),repmat(R,[1,npred_i])]);
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f=zeros(nstates*(npred_i+1)+ninputs*npred_i,1);
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[x,fval] = quadprog(H,f,[],[],Aeq,beq,Lb,Ub);
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%get linearized input
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u_mpc(:,i)=x(nstates*(npred_i+1)+1:nstates*(npred_i+1)+ninputs);
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%get input
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U(:,i)=u_mpc(:,i)+U_ref(:,i);
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%simulate model
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[~,ztemp]=ode45(@(t,z)kinematic_bike(t,z,U(:,i),0),[0 dt],Y(:,i));
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%store final state
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Y(:,i+1)=ztemp(end,:)';
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end
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% while (index_cl_cp < size(TestTrack.cline,2))
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% %because of the way cf/nc need to be)
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% index_cl_cp
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% iter = iter + 1;
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% [lb, ub] = bounds(nsteps, index_cl_cp);
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% cf=@costfun
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% nc=@nonlcon
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% z=fmincon(cf,x0vec,[],[],[],[],lb',ub',nc,options);
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% Y0=reshape(z(1:6*nsteps),6,nsteps)';
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% U=reshape(z(6*nsteps+1:end),2,nsteps-1)';
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%
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% [Y, T] = forwardIntegrateControlInput(U, x0vec(1:6));
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%
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% curr_xy = [Y(end,1); Y(end,3)];
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% dist2cp1 = norm(curr_xy - TestTrack.cline(:, index_cl_cp));
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% dist2cp2 = norm(curr_xy - TestTrack.cline(:, index_cl_nextcp));
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%
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% if (dist2cp2 < (dist2cp1 - dist2cp1*factor))
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% dist12atcp = [dist12atcp; dist2cp1, dist2cp2];
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% %add to final solution
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% U_final = [U_final; U];
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% Y_final = [Y_final; Y];
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%
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% %reinstantiate
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% %nsteps = 100;
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% x0vec = initvec(Y_final(end,:), U_final(end,:));
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%
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% %update checkpoint
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% index_cl_cp = index_cl_cp + 1
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% index_cl_nextcp = index_cl_nextcp + 1;
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% if (index_cl_nextcp > size(TestTrack.cline, 2))
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% index_cl_nextcp = size(TestTrack.cline, 2);
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% end
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% else
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% %resize and try again
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% %nsteps = nsteps + 20;
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% x0vec = initvec(x0vec(1:6), U(1,:));
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%
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% end
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%
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% end
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%% function start
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function [start_idx, end_idx] = get_indices(segment_num, num_pts)
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if segment_num == 1
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@@ -166,6 +162,7 @@ function [start_idx, end_idx] = get_indices(segment_num, num_pts)
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end
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end
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%not used
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function x0vec = initvec(x0, u0)
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%function used because fmincon needs initial condition to be size of
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%state vector
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@@ -184,6 +181,115 @@ function x0vec = initvec(x0, u0)
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end
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end
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%% mpc functions
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function [Aeq,beq]=eq_cons(initial_idx,A,B,x_initial,npred,nstates,ninputs)
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%build matrix for A_i*x_i+B_i*u_i-x_{i+1}=0
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%in the form Aeq*z=beq
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%initial_idx specifies the time index of initial condition from the reference trajectory
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%A and B are function handles above
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%initial condition
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x_initial=x_initial(:);
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%size of decision variable and size of part holding states
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zsize=(npred+1)*nstates+npred*ninputs;
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xsize=(npred+1)*nstates;
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Aeq=zeros(xsize,zsize);
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Aeq(1:nstates,1:nstates)=eye(nstates); %initial condition
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beq=zeros(xsize,1);
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beq(1:nstates)=x_initial;
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state_idxs=nstates+1:nstates:xsize;
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input_idxs=xsize+1:ninputs:zsize;
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for i=1:npred
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%negative identity for i+1
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Aeq(state_idxs(i):state_idxs(i)+nstates-1,state_idxs(i):state_idxs(i)+nstates-1)=-eye(nstates);
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%A matrix for i
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Aeq(state_idxs(i):state_idxs(i)+nstates-1,state_idxs(i)-nstates:state_idxs(i)-1)=A(initial_idx+i-1);
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%B matrix for i
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Aeq(state_idxs(i):state_idxs(i)+nstates-1,input_idxs(i):input_idxs(i)+ninputs-1)=B(initial_idx+i-1);
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end
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end
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function [Lb,Ub]=bound_cons(initial_idx,U_ref,npred,input_range,nstates,ninputs)
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%time_idx is the index along uref the initial condition is at
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xsize=(npred+1)*nstates;
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usize=npred*ninputs;
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Lb=[];
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Ub=[];
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for j=1:ninputs
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Lb=[Lb;input_range(j,1)-U_ref(j,initial_idx:initial_idx+npred-1)];
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Ub=[Ub;input_range(j,2)-U_ref(j,initial_idx:initial_idx+npred-1)];
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end
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Lb=reshape(Lb,[usize,1]);
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Ub=reshape(Ub,[usize,1]);
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Lb=[-Inf(xsize,1);Lb];
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Ub=[Inf(xsize,1);Ub];
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end
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function dzdt=kinematic_bike(t,x,U,T)
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%constants
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Nw=2;
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f=0.01;
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Iz=2667;
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a=1.35;
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b=1.45;
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By=0.27;
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Cy=1.2;
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Dy=0.7;
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Ey=-1.6;
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Shy=0;
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Svy=0;
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m=1400;
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g=9.806;
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%generate input functions
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delta_f=U(1);
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F_x=U(2);
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%slip angle functions in degrees
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a_f=rad2deg(delta_f-atan2(x(4)+a*x(6),x(2)));
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a_r=rad2deg(-atan2((x(4)-b*x(6)),x(2)));
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%Nonlinear Tire Dynamics
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phi_yf=(1-Ey)*(a_f+Shy)+(Ey/By)*atan(By*(a_f+Shy));
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phi_yr=(1-Ey)*(a_r+Shy)+(Ey/By)*atan(By*(a_r+Shy));
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F_zf=b/(a+b)*m*g;
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F_yf=F_zf*Dy*sin(Cy*atan(By*phi_yf))+Svy;
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F_zr=a/(a+b)*m*g;
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F_yr=F_zr*Dy*sin(Cy*atan(By*phi_yr))+Svy;
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F_total=sqrt((Nw*F_x)^2+(F_yr^2));
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F_max=0.7*m*g;
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if F_total>F_max
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F_x=F_max/F_total*F_x;
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F_yr=F_max/F_total*F_yr;
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end
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%vehicle dynamics
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dzdt= [x(2)*cos(x(5))-x(4)*sin(x(5));...
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(-f*m*g+Nw*F_x-F_yf*sin(delta_f))/m+x(4)*x(6);...
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x(2)*sin(x(5))+x(4)*cos(x(5));...
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(F_yf*cos(delta_f)+F_yr)/m-x(2)*x(6);...
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x(6);...
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(F_yf*a*cos(delta_f)-F_yr*b)/Iz];
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end
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%% nonlinear opt functions
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function [lb, ub] = bounds(start_idx, end_idx)
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load('TestTrack.mat');
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load('reftrack_info.mat');
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@@ -361,6 +467,8 @@ function [g, h, dg, dh] = nonlcon(z)
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end
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%% linearization functions
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function dzdt=bike(x,U)
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%constants
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Nw=2;
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