38 lines
1.3 KiB
Mathematica
38 lines
1.3 KiB
Mathematica
|
function out = traj_cost_lgr(opt_vars,traj_par,ur10)
|
||
|
|
% -------------------------------------------------------------------
|
||
|
|
% This function computes cost in terms of condition number for
|
||
|
|
% trajectory optimization needed for dynamic parameter identification
|
||
|
|
% The computation of regressor matrix is obtained using screw
|
||
|
|
% theory methods.
|
||
|
|
% -------------------------------------------------------------------
|
||
|
|
|
||
|
|
% Trajectory parameters
|
||
|
|
N = traj_par.N;
|
||
|
|
wf = traj_par.wf;
|
||
|
|
T = traj_par.T;
|
||
|
|
t = traj_par.t;
|
||
|
|
|
||
|
|
% As paramters of the trajectory are in a signle vector we reshape them as
|
||
|
|
% to feed the function that computes the trajectory
|
||
|
|
ab = reshape(opt_vars,[12,N]);
|
||
|
|
a = ab(1:6,:); % sin coeffs
|
||
|
|
b = ab(7:12,:); % cos coeffs
|
||
|
|
|
||
|
|
% To guarantee that positions, velocities and accelerations are zero in the
|
||
|
|
% beginning and at time T, we add fifth order polynomial to fourier
|
||
|
|
% series. The parameters of the polynomial depends on the parameters of
|
||
|
|
% fourier series. Here we compute them.
|
||
|
|
c_pol = getPolCoeffs(T, a, b, wf, N, ur10.q0);
|
||
|
|
|
||
|
|
% Compute trajectory (Fouruer series + fifth order polynomail)
|
||
|
|
[q,qd,q2d] = mixed_traj(t, c_pol, a, b, wf, N);
|
||
|
|
|
||
|
|
% Obtain observation matrix by computing regressor for each sampling time
|
||
|
|
W = [];
|
||
|
|
for i = 1:length(t)
|
||
|
|
Y = base_regressor_UR10E(q(:,i),qd(:,i),q2d(:,i));
|
||
|
|
W = vertcat(W,Y);
|
||
|
|
end
|
||
|
|
|
||
|
|
out = cond(W);
|