def test_constructors_initialisations(self):
ps = copra.PreviewSystem()
ps.system(self.A, self.B, self.c, self.x0, self.nbStep)
controller = copra.LMPC(ps)
copra.LMPC()
copra.LMPC(copra.SolverFlag.QuadProgDense)
copra.LMPC(ps, copra.SolverFlag.QuadProgDense)
controller.initialize_controller(ps)
def test_lmpc_ineq(self):
ps = copra.PreviewSystem()
ps.system(self.A, self.B, self.c, self.x0, self.nbStep)
controller = copra.LMPC(ps)
xCost = copra.TargetCost(self.M, -self.xd)
uCost = copra.ControlCost(self.N, -self.ud)
trajConstr = copra.TrajectoryConstraint(self.Eineq, self.fineq)
contConstr = copra.ControlConstraint(self.Gineq, self.hineq)
xCost.weights(self.wx)
uCost.weights(self.wu)
controller.add_cost(xCost)
controller.add_cost(uCost)
controller.add_constraint(trajConstr)
controller.add_constraint(contConstr)
self.assertTrue(controller.solve())
control = controller.control()
fullTraj = controller.trajectory()
fTLen = int(len(fullTraj) / 2)
posTraj = [0.] * fTLen
velTraj = [0.] * fTLen
for i in range(fTLen):
posTraj[i] = fullTraj[2 * i]
velTraj[i] = fullTraj[2 * i + 1]
self.assertAlmostEqual(self.xd[1], velTraj[-1], places=3)
self.assertLessEqual(max(posTraj), self.x0[0])
self.assertLessEqual(np.amax(control), self.hineq[0])
print("Test lmpc with inequalities")
print(controller.solve_time(), "s")
print(controller.solve_and_build_time(), "s")
print()
def test_throw_handler(self):
ps = copra.PreviewSystem()
ps.system(self.A, self.B, self.c, self.x0, self.nbStep)
controller = copra.LMPC(ps)
# Test trajectory constraint throws
with self.assertRaises(RuntimeError):
constr = copra.TrajectoryConstraint(np.identity(5), np.ones((2, )))
controller.add_constraint(constr)
# Test control constraint throws
with self.assertRaises(RuntimeError):
constr = copra.ControlConstraint(np.identity(5), np.ones((2, )))
controller.add_constraint(constr)
# Test mixed constraint throws
with self.assertRaises(RuntimeError):
constr = copra.MixedConstraint(np.identity(5), np.identity(5),
np.ones((2, )))
controller.add_constraint(constr)
# Test trajectory bound constraint throws
with self.assertRaises(RuntimeError):
constr = copra.TrajectoryBoundConstraint(np.ones((3, )),
np.ones((2, )))
controller.add_constraint(constr)
# Test control bound constraint throws
with self.assertRaises(RuntimeError):
constr = copra.ControlBoundConstraint(np.ones((3, )), np.ones(
(2, )))
controller.add_constraint(constr)
def test_preview_systeme_still_exist(self):
ps = copra.PreviewSystem()
ps.system(self.A, self.B, self.c, self.x0, self.nbStep)
controller = copra.LMPC(ps)
del ps
trajConstr = copra.TrajectoryConstraint(self.Eineq, self.fineq)
contConstr = copra.ControlConstraint(self.Gineq, self.hineq)
targetCost = copra.TargetCost(self.M, -self.xd)
controlCost = copra.ControlCost(self.N, -self.ud)
targetCost.weights(self.wx)
controlCost.weights(self.wu)
controller.add_constraint(trajConstr)
controller.add_constraint(contConstr)
controller.add_cost(targetCost)
controller.add_cost(controlCost)
self.assertTrue(controller.solve())
control = controller.control()
fullTraj = controller.trajectory()
fTLen = int(len(fullTraj) / 2)
posTraj = [0.] * fTLen
velTraj = [0.] * fTLen
for i in range(fTLen):
posTraj[i] = fullTraj[2 * i]
velTraj[i] = fullTraj[2 * i + 1]
self.assertAlmostEqual(self.xd[1], velTraj[-1], places=3)
self.assertLessEqual(max(posTraj), self.x0[0])
self.assertLessEqual(np.amax(control), self.hineq[0])
def test_lmpc_eq(self):
print "test_lmpc_eq"
ps = pyCopra.PreviewSystem()
ps.system(self.A, self.B, self.c, self.x0Eq, self.nbStep)
controller = pyCopra.LMPC(ps)
xCost = pyCopra.TargetCost(self.M, -self.xdEq)
uCost = pyCopra.ControlCost(self.N, -self.ud)
trajConstr = pyCopra.TrajectoryConstraint(self.Eeq, self.feq, False)
xCost.weights(self.wx)
uCost.weights(self.wu)
controller.add_cost(xCost)
controller.add_cost(uCost)
controller.add_constraint(trajConstr)
self.assertTrue(controller.solve())
control = controller.control()
fullTraj = controller.trajectory()
fTLen = len(fullTraj) / 2
posTraj = [0.] * fTLen
velTraj = [0.] * fTLen
for i in xrange(fTLen):
posTraj[i] = fullTraj[2 * i]
velTraj[i] = fullTraj[2 * i + 1]
self.assertAlmostEqual(self.xd[1], velTraj[-1], places=3)
self.assertLessEqual(max(posTraj), self.x0[0] + 1e-6)
self.assertLessEqual(max(velTraj), self.feq[0] + 1e-6)
print "Test lmpc with equalities"
print controller.solve_time(), "s"
print controller.solve_and_build_time(), "s"
print
def test_constraint_and_cost_deletion(self):
print("Testing 'test_constraint_deletion'.")
print(
"In order to see the outputs, the copra must be installed under Debug mode."
)
ps = copra.PreviewSystem()
ps.system(self.A, self.B, self.c, self.x0, self.nbStep)
controller = copra.LMPC(ps)
trajConstr = copra.TrajectoryConstraint(self.Eineq, self.fineq)
contConstr = copra.ControlConstraint(self.Gineq, self.hineq)
trajEqConstr = copra.TrajectoryConstraint(self.Eeq, self.feq, False)
contEqConstr = copra.ControlConstraint(self.Geq, self.heq, False)
trajBdConstr = copra.TrajectoryBoundConstraint(self.xLower,
self.xUpper)
contBdConstr = copra.ControlBoundConstraint(self.uLower, self.uUpper)
targetCost = copra.TargetCost(self.M, -self.xd)
trajectoryCost = copra.TrajectoryCost(self.M, -self.xd)
controlCost = copra.ControlCost(self.N, -self.ud)
M_mixed = np.ones((1, 2))
mixedCost = copra.MixedCost(M_mixed, self.N, -self.ud)
controller.add_constraint(trajConstr)
controller.add_constraint(contConstr)
controller.add_constraint(trajEqConstr)
controller.add_constraint(contEqConstr)
controller.add_constraint(trajBdConstr)
controller.add_constraint(contBdConstr)
controller.add_cost(targetCost)
controller.add_cost(trajectoryCost)
controller.add_cost(controlCost)
controller.add_cost(mixedCost)
del trajConstr
targetCost.weights(self.wx)
controlCost.weights(self.wu)
del trajEqConstr
del contEqConstr
del trajBdConstr
del contBdConstr
del trajectoryCost
del mixedCost
self.assertFalse(controller.solve())
self.assertTrue(controller.solve()) # Has kept the contConstr only
def test_constructors_initialisations(self):
print "test_constructors_initialisations"
ps = pyCopra.PreviewSystem()
ps.system(self.A, self.B, self.c, self.x0, self.nbStep)
controller = pyCopra.LMPC(ps)
pyCopra.LMPC()
if hasattr(pyCopra.SolverFlag, 'QuadProgDense'):
pyCopra.LMPC(pyCopra.SolverFlag.QuadProgDense)
pyCopra.LMPC(ps, pyCopra.SolverFlag.QuadProgDense)
elif hasattr(pyCopra.SolverFlag, 'QLD'):
pyCopra.LMPC(pyCopra.SolverFlag.QLD)
pyCopra.LMPC(ps, pyCopra.SolverFlag.QLD)
else:
pyCopra.LMPC(pyCopra.SolverFlag.Default)
pyCopra.LMPC(ps, pyCopra.SolverFlag.Default)
controller.initialize_controller(ps)
def test_lmpc_mixed(self):
print "test_lmpc_mixed"
ps = pyCopra.PreviewSystem()
ps.system(self.A, self.B, self.c, self.x0, self.nbStep)
controller = pyCopra.LMPC(ps)
xCost = pyCopra.TargetCost(self.M, -self.xd)
uCost = pyCopra.ControlCost(self.N, -self.ud)
mixedConstr = pyCopra.MixedConstraint(self.Eineq, self.Gineq,
self.hineq)
xCost.weights(self.wx)
uCost.weights(self.wu)
controller.add_cost(xCost)
controller.add_cost(uCost)
controller.add_constraint(mixedConstr)
self.assertTrue(controller.solve())
control = controller.control()
fullTraj = controller.trajectory()
fTLen = len(fullTraj) / 2
posTraj = [0.] * fTLen
velTraj = [0.] * fTLen
for i in xrange(fTLen):
posTraj[i] = fullTraj[2 * i]
velTraj[i] = fullTraj[2 * i + 1]
self.assertAlmostEqual(self.xd[1], velTraj[-1], places=3)
self.assertLessEqual(max(posTraj), self.x0[0])
for i in xrange(self.Eineq.shape[0]):
res = 0
for j in xrange(self.Eineq.shape[1]):
res += self.Eineq[i, j] * fullTraj[i * self.Eineq.shape[1] + j]
for j in xrange(self.Gineq.shape[1]):
res += self.Gineq[i, j] * control[i * self.Gineq.shape[1] + j]
self.assertLessEqual(res, self.hineq[0])
print "Test lmpc with inequalities"
print controller.solve_time(), "s"
print controller.solve_and_build_time(), "s"
print
def test_dynamic_walk(self):
A = np.array([[1, 0, 0, 0.11699999999999999, 0, 0],
[0, 1, 0, 0, 0.11699999999999999, 0],
[0, 0, 1, 0, 0, 0.11699999999999999], [0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 1]])
B = np.array([[0.006844499999999999, 0,
0], [0, 0.006844499999999999, 0],
[0, 0, 0.006844499999999999],
[0.11699999999999999, 0, 0], [0, 0.11699999999999999, 0],
[0, 0, 0.11699999999999999]])
c = np.array([0., 0., 0., 0., 0., 0.])
x_init = np.array([
1.5842778860957882, 0.3422260214935311, 2.289067474385933, 0., 0.,
0.
])
x_goal = np.array([
1.627772868473883, 0.4156386515475985, 2.3984423755527136,
0.06745225960685897, 0.3882830795737303, 0.06845759848745198
])
nb_steps = 10
G = np.array([
[
-1, 9.946646523934742, -4.870790074510924, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
-18.826459196882055, 3.4468275392859393, -1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
-1.374181960437557, -8.028252906078723, -1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
-9.936224732113594, 5.000580301294253, -1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
8.750597187695343, -4.7538557382857105, -1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
18.65430148414319, 1, -5.084871935334947, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
2.1775137248880574e-15, -5.443784312220143e-16, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0
],
[
0, 0, 0, -1, 9.946646523934742, -4.870790074510924, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, -18.826459196882055, 3.4468275392859393, -1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, -1.374181960437557, -8.028252906078723, -1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, -9.936224732113594, 5.000580301294253, -1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 8.750597187695343, -4.7538557382857105, -1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 18.65430148414319, 1, -5.084871935334947, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 2.1775137248880574e-15, -5.443784312220143e-16, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0
],
[
0, 0, 0, 0, 0, 0, -1, 9.946646523934742, -4.870790074510924, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, -18.826459196882055, 3.4468275392859393, -1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, -1.374181960437557, -8.028252906078723, -1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, -9.936224732113594, 5.000580301294253, -1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 8.750597187695343, -4.7538557382857105, -1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 18.65430148414319, 1, -5.084871935334947, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 2.1775137248880574e-15,
-5.443784312220143e-16, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 9.946646523934742,
-4.870790074510924, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, -18.826459196882055,
3.4468275392859393, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, -1.374181960437557,
-8.028252906078723, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, -9.936224732113594,
5.000580301294253, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 8.750597187695343,
-4.7538557382857105, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 18.65430148414319, 1,
-5.084871935334947, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1775137248880574e-15,
-5.443784312220143e-16, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 9.946646523934742,
-4.870790074510924, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -18.826459196882055,
3.4468275392859393, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.374181960437557,
-8.028252906078723, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -9.936224732113594,
5.000580301294253, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8.750597187695343,
-4.7538557382857105, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18.65430148414319, 1,
-5.084871935334947, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1775137248880574e-15,
-5.443784312220143e-16, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1,
9.946646523934742, -4.870790074510924, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-18.826459196882055, 3.4468275392859393, -1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-1.374181960437557, -8.028252906078723, -1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-9.936224732113594, 5.000580301294253, -1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8.750597187695343,
-4.7538557382857105, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18.65430148414319,
1, -5.084871935334947, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
2.1775137248880574e-15, -5.443784312220143e-16, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
8.750597218241072, -4.753855754313641, -1, 0, 0, 0, 0, 0, 0, 0,
0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-1.3741819771739483, -8.028252929943818, -1, 0, 0, 0, 0, 0, 0,
0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-18.82645925631406, 3.4468275193254927, -1, 0, 0, 0, 0, 0, 0,
0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1.1824397341134247, 7.4638136184143935, -1, 0, 0, 0, 0, 0, 0,
0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
14.006645137157978, -2.2569159229140494, -1, 0, 0, 0, 0, 0, 0,
0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
2.1775137248880578e-15, -5.443784312220144e-16, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
8.750597218241072, -4.753855754313641, -1, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-1.3741819771739483, -8.028252929943818, -1, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-18.82645925631406, 3.4468275193254927, -1, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1.1824397341134247, 7.4638136184143935, -1, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
14.006645137157978, -2.2569159229140494, -1, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
2.1775137248880578e-15, -5.443784312220144e-16, 1, 0, 0, 0, 0,
0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 8.750597218241072, -4.753855754313641, -1, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, -1.3741819771739483, -8.028252929943818, -1, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, -18.82645925631406, 3.4468275193254927, -1, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1.1824397341134247, 7.4638136184143935, -1, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 14.006645137157978, -2.2569159229140494, -1, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 2.1775137248880578e-15, -5.443784312220144e-16, 1, 0,
0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 8.750597218241072, -4.753855754313641, -1
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, -1.3741819771739483, -8.028252929943818, -1
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, -18.82645925631406, 3.4468275193254927, -1
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1.1824397341134247, 7.4638136184143935, -1
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 14.006645137157978, -2.2569159229140494, -1
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 2.1775137248880578e-15,
-5.443784312220144e-16, 1
]
])
h = np.array([
47.76613348420254, 9.80665, 9.80665, 9.80665, 9.806649999999998,
49.86555936465245, 9.806649999999994, 47.76613348420254, 9.80665,
9.80665, 9.80665, 9.806649999999998, 49.86555936465245,
9.806649999999994, 47.76613348420254, 9.80665, 9.80665, 9.80665,
9.806649999999998, 49.86555936465245, 9.806649999999994,
47.76613348420254, 9.80665, 9.80665, 9.80665, 9.806649999999998,
49.86555936465245, 9.806649999999994, 47.76613348420254, 9.80665,
9.80665, 9.80665, 9.806649999999998, 49.86555936465245,
9.806649999999994, 47.76613348420254, 9.80665, 9.80665, 9.80665,
9.806649999999998, 49.86555936465245, 9.806649999999994,
9.806650000000007, 9.806650000000008, 9.80665, 9.806650000000001,
9.806650000000007, 9.806649999999996, 9.806650000000007,
9.806650000000008, 9.80665, 9.806650000000001, 9.806650000000007,
9.806649999999996, 9.806650000000007, 9.806650000000008, 9.80665,
9.806650000000001, 9.806650000000007, 9.806649999999996,
9.806650000000007, 9.806650000000008, 9.80665, 9.806650000000001,
9.806650000000007, 9.806649999999996
])
ps = copra.PreviewSystem()
ps.system(A, B, c, x_init, nb_steps)
controller = copra.LMPC(ps)
contConstr = copra.ControlConstraint(G, h)
M_cost = np.identity(6)
targetCost = copra.TargetCost(M_cost, -x_goal)
controller.add_constraint(contConstr)
controller.add_cost(targetCost)
controller.solve()
print(controller.solve_time(), "s")
