jgillis

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jgillis

% Load the matrices from files
actual = casadi.Sparsity.from_file('debug_fatrop_actual.mtx');
A = casadi.Sparsity.from_file('debug_fatrop_A.mtx');
B = casadi.Sparsity.from_file('debug_fatrop_B.mtx');
C = casadi.Sparsity.from_file('debug_fatrop_C.mtx');
D = casadi.Sparsity.from_file('debug_fatrop_D.mtx');
I = casadi.Sparsity.from_file('debug_fatrop_I.mtx');
errors = row(casadi.Sparsity.from_file('debug_fatrop_errors.mtx'));

% Create a new figure

jgillis

from casadi import *

# Example on how to use the DaeBuilder class
# Joel Andersson, UW Madison 2017

# Start with an empty DaeBuilder instance
dae = DaeBuilder('rocket')

# Add input expressions

jgillis

/* This file was automatically generated by CasADi 3.6.5+.
 *  It consists of: 
 *   1) content generated by CasADi runtime: not copyrighted
 *   2) template code copied from CasADi source: permissively licensed (MIT-0)
 *   3) user code: owned by the user
 *
 */
#ifdef __cplusplus
extern "C" {
#endif

jgillis

from casadi import *

# Generate 3D data to fit
x = np.linspace(0,2,10)
y = np.linspace(2,4,10)
z = np.linspace(3,5,10)

[X,Y,Z] = np.meshgrid(x,y,z)

xyz_flat = np.vstack((X.ravel(),Y.ravel(),Z.ravel())).T

jgillis

from casadi import *

opti = Opti()

x = opti.variable(10)
y = opti.variable(10)

opti.subject_to(x>=0)
opti.subject_to(x+y==0)
opti.subject_to(y>=0)

jgillis

m = 10 # Limite memory size

x_latest = MX.sym("x_latest",nx)
g_latest = MX.sym("g_latest",nx)

x_prev = MX.sym("x_prev",nx)
g_prev = MX.sym("g_prev",nx)

s_latest = x_latest-x_prev
y_latest = g_latest-g_prev

jgillis

import casadi as cs
import numpy as np

xp = [1, 2, 3]
fp = [3, 2, 0]

x = [2.5,1.1]

print(np.interp(x=x,xp=xp,fp=fp))

jgillis

XYZ_ref = [ simOut.EndEffector_AbsPos{1}.Values.Data,simOut.EndEffector_AbsPos{2}.Values.Data,simOut.EndEffector_AbsPos{3}.Values.Data ]';

Nsim = size(Gantry_out_D.Data,1);
XYZ = zeros(3,Nsim);
for k=1:Nsim
XYZ(:,k) = ForwardKinematicsFun(Gantry_out_D.Data(k,2),...
                     Axis1_Out_D.Data(k,2)/180*pi,...
                     Axis2_Out_D.Data(k,2)/180*pi,...
                     Axis3_Out_D.Data(k,2)/180*pi,...
                     Axis4_Out_D.Data(k,2)/180*pi,...

jgillis

from casadi import *

x = MX.sym("x",30)
x0 = DM.rand(x.shape)

print("Reference approach")

# Repetitive
c = 0