def smith_step(A, t, var):
# erste Spalte (Index: j), die nicht komplett 0 ist
# j soll größer als Schrittzähler sein
nr, nc = A.shape
row_op_list = []
cols = st.col_split(A)
# erste nicht verschwindende Spalte finden
for j, c in enumerate(cols):
if j < t:
continue
if not c == c*0:
break
# Eintrag mit Index t soll ungleich 0 sein, ggf. Zeilen tauschen
if c[t] == 0:
i, elt = first_nonzero_element(c)
ro = row_swap(nr, t, i)
c = ro*c
row_op_list.append(ro)
col = c.expand()
while True:
new_col, L0 = smith_column_step(col, t, var)
if L0 == sp.eye(nr):
# nothing has changed
break
row_op_list.append(L0)
col = new_col
# jetzt teilt col[t] alle Einträge in col
# Probe in der nächsten Schleife
col.simplify()
col = col.expand()
for i,a in enumerate(col):
if i == t:
continue
if not sp.simplify(sp.gcd(a, col[t]) - col[t]) == 0:
IPS()
raise ValueError, "col[t] should divide all entries in col"
quotient = sp.simplify(a/col[t])
if a == 0:
continue
# eliminiere a
ro = row_op(nr, i, t, -1, quotient)
row_op_list.append(ro)
return row_op_list
def test_simple_pendulum_with_actuated_mountpoint(self):
np = 1
nq = 2
n = np + nq
pp = st.symb_vector("p1:{0}".format(np+1))
qq = st.symb_vector("q1:{0}".format(nq+1))
p1, q1, q2 = ttheta = st.row_stack(pp, qq)
pdot1, qdot1, qdot2 = tthetad = st.time_deriv(ttheta, ttheta)
mud = st.time_deriv(ttheta, ttheta, order=2)
params = sp.symbols('l3, l4, s3, s4, J3, J4, m1, m2, m3, m4, g')
l3, l4, s3, s4, J3, J4, m1, m2, m3, m4, g = params
tau1, tau2 = ttau= st.symb_vector("tau1, tau2")
## Geometry
ex = sp.Matrix([1,0])
ey = sp.Matrix([0,1])
# Koordinaten der Schwerpunkte und Gelenke
S1 = ex*q1
S2 = ex*q1 + ey*q2
G3 = S2 # Gelenk
# Schwerpunkt des Pendels #zeigt nach oben
S3 = G3 + mt.Rz(p1)*ey*s3
# Zeitableitungen der Schwerpunktskoordinaten
Sd1, Sd2, Sd3 = st.col_split(st.time_deriv(st.col_stack(S1, S2, S3), ttheta)) ##
# Energy
T_rot = ( J3*pdot1**2 )/2
T_trans = ( m1*Sd1.T*Sd1 + m2*Sd2.T*Sd2 + m3*Sd3.T*Sd3 )/2
T = T_rot + T_trans[0]
V = m1*g*S1[1] + m2*g*S2[1] + m3*g*S3[1]
external_forces = [0, tau1, tau2]
assert not any(external_forces[:np])
mod = mt.generate_symbolic_model(T, V, ttheta, external_forces)
mod.calc_coll_part_lin_state_eq(simplify=True)
#pdot1, qdot1, qdot2 = mod.ttd
ff_ref = sp.Matrix([[pdot1], [qdot1], [qdot2], [g*m3*s3*sin(p1)/(J3 + m3*s3**2)], [0], [0]])
gg_ref_part = sp.Matrix([m3*s3*cos(p1)/(J3 + m3*s3**2), m3*s3*sin(p1)/(J3 + m3*s3**2)]).T
self.assertEqual(mod.ff, ff_ref)
self.assertEqual(mod.gg[-3, :], gg_ref_part)
def test_four_bar_constraints(self):
# 2 passive Gelenke
np = 2
nq = 1
p1, p2 = pp = st.symb_vector("p1:{0}".format(np + 1))
q1, = qq = st.symb_vector("q1:{0}".format(nq + 1))
ttheta = st.row_stack(pp, qq)
pdot1, pdot2, qdot1 = tthetad = st.time_deriv(ttheta, ttheta)
tthetadd = st.time_deriv(ttheta, ttheta, order=2)
st.make_global(ttheta, tthetad, tthetadd)
params = sp.symbols(
's1, s2, s3, m1, m2, m3, J1, J2, J3, l1, l2, l3, kappa1, kappa2, g'
)
s1, s2, s3, m1, m2, m3, J1, J2, J3, l1, l2, l3, kappa1, kappa2, g = params
st.make_global(params)
parameter_values = dict(s1=1 / 2,
s2=1 / 2,
s3=1 / 2,
m1=1,
m2=1,
m3=3,
J1=1 / 12,
J2=1 / 12,
J3=1 / 12,
l1=1,
l2=1.5,
l3=1.5,
kappa1=3 / 2,
kappa2=14.715,
g=9.81).items()
# ttau = sp.symbols('tau')
tau1, tau2 = st.symb_vector("tau1, tau2")
# unit vectors
ex = sp.Matrix([1, 0])
# Basis 1 and 2
# B1 = sp.Matrix([0, 0])
B2 = sp.Matrix([2 * l1, 0])
# Koordinaten der Schwerpunkte und Gelenke
S1 = Rz(q1) * ex * s1
G1 = Rz(q1) * ex * l1
S2 = G1 + Rz(q1 + p1) * ex * s2
G2 = G1 + Rz(q1 + p1) * ex * l2
# one link manioulator
G2b = B2 + Rz(p2) * ex * l3
S3 = B2 + Rz(p2) * ex * s3
# timederivatives of centers of masses
Sd1, Sd2, Sd3 = st.col_split(
st.time_deriv(st.col_stack(S1, S2, S3), ttheta))
# kinetic energy
T_rot = (J1 * qdot1**2 + J2 * (qdot1 + pdot1)**2 + J3 * (pdot2)**2) / 2
T_trans = (m1 * Sd1.T * Sd1 + m2 * Sd2.T * Sd2 + m3 * Sd3.T * Sd3) / 2
T = T_rot + T_trans[0]
# potential energy
V = m1 * g * S1[1] + m2 * g * S2[1] + m3 * g * S3[1]
# # Kinetische Energie mit Platzhaltersymbolen einführen (jetzt nicht):
#
# M1, M2, M3 = MM = st.symb_vector('M1:4')
# MM_subs = [(J1 + m1*s1 ** 2 + m2*l1 ** 2, M1), (J2 + m2*s2 ** 2, M2), (m2*l1*s2, M3)]
# # MM_rplm = st.rev_tuple(MM_subs) # Umkehrung der inneren Tupel -> [(M1, J1+... ), ...]
external_forces = [0, 0, tau1]
mod = mt.generate_symbolic_model(T,
V,
ttheta,
external_forces,
constraints=[G2 - G2b])
self.assertEqual(mod.llmd.shape, (2, 1))
self.assertEqual(mod.constraints.shape, (2, 1))
dae = mod.calc_dae_eq(parameter_values=parameter_values)
nc = len(mod.llmd)
self.assertEqual(dae.eqns[-nc:, :], mod.constraints)
# qdot1 = 0
ttheta_1, ttheta_d_1 = dae.calc_consistent_conf_vel(q1=npy.pi / 4,
_disp=False)
eres_c = npy.array([-0.2285526, 1.58379902, 0.78539816])
eres_v = npy.array([0, 0, 0])
self.assertTrue(npy.allclose(ttheta_1, eres_c))
self.assertTrue(npy.allclose(ttheta_d_1, eres_v))
# in that situation there is no force
acc_1, llmd_1 = dae.calc_consistent_accel_lmd((ttheta_1, ttheta_d_1))
# these values seem reasonable but have yet not been checked analytically
self.assertTrue(
npy.allclose(acc_1, [13.63475466, -1.54473017, -8.75145644]))
self.assertTrue(npy.allclose(llmd_1, [-0.99339947, 0.58291489]))
# qdot1 ≠ 0
ttheta_2, ttheta_d_2 = dae.calc_consistent_conf_vel(q1=npy.pi / 8 * 7,
qdot1=3,
_disp=False)
eres_c = npy.array([-0.85754267, 0.89969149, 0.875]) * npy.pi
eres_v = npy.array([-3.42862311, 2.39360715, 3.])
self.assertTrue(npy.allclose(ttheta_2, eres_c))
self.assertTrue(npy.allclose(ttheta_d_2, eres_v))
yy_1, yyd_1 = dae.calc_consistent_init_vals(q1=12.7, qdot1=100)
dae.generate_eqns_funcs()
res = dae.model_func(0, yy_1, yyd_1)
self.assertEqual(res.shape, (dae.ntt * 2 + dae.nll, ))
self.assertTrue(npy.allclose(res, 0))
ex = sp.Matrix([1,0]) ey = sp.Matrix([0,1]) # Koordinaten der Schwerpunkte und Gelenke S0 = ex*q1 # Schwerpunkt Wagen G0 = S0 # Gelenk zwischen Wagen und Pendel 1 G1 = G0 + mt.Rz(p1)*ey*l1 # Gelenk zwischen Pendel 1 und 2 G2 = G1 + mt.Rz(p1+p2)*ey*l2 # Gelenk zwischen Pendel 2 und 3 # Schwerpunkte der Pendel (Pendel zeigen für kleine Winkel nach oben; Pendelwinkel relativ) S1 = G0 + mt.Rz(p1)*ey*s1 S2 = G1 + mt.Rz(p1+p2)*ey*s2 S3 = G2 + mt.Rz(p1+p2+p3)*ey*s3 # Zeitableitungen der Schwerpunktskoordinaten Sd0, Sd1, Sd2, Sd3 = st.col_split(st.time_deriv(st.col_stack(S0, S1, S2, S3), ttheta)) # Energie T_rot = (J1*pdot1**2)/2 + (J2*(pdot1 + pdot2)**2)/2 + (J3*(pdot1 + pdot2 + pdot3)**2)/2 T_trans = (m0*Sd0.T*Sd0 + m1*Sd1.T*Sd1 + m2*Sd2.T*Sd2 + m3*Sd3.T*Sd3)/2 T = T_rot + T_trans[0] V = m1*g*S1[1] + m2*g*S2[1] + m3*g*S3[1] mod = mt.generate_symbolic_model(T, V, ttheta, [0, 0, 0, tau1]) # Zustandsraummodell, partiell linearisiert mod.calc_coll_part_lin_state_eq(simplify=True) x_dot = mod.ff + mod.gg*qddot1
def solve(problem_spec):
# problem_spec is dummy
t = sp.Symbol('t') # time variable
np = 2
nq = 2
ns = 2
n = np + nq + ns
p1, p2 = pp = st.symb_vector("p1:{0}".format(np + 1))
q1, q2 = qq = st.symb_vector("q1:{0}".format(nq + 1))
s1, s2 = ss = st.symb_vector("s1:{0}".format(ns + 1))
ttheta = st.row_stack(qq[0], pp[0], ss[0], qq[1], pp[1], ss[1])
qdot1, pdot1, sdot1, qdot2, pdot2, sdot2 = tthetad = st.time_deriv(ttheta, ttheta)
tthetadd = st.time_deriv(ttheta, ttheta, order=2)
ttheta_all = st.concat_rows(ttheta, tthetad, tthetadd)
c1, c2, c3, c4, c5, c6, m1, m2, m3, m4, m5, m6, J1, J2, J3, J4, J5, J6, l1, l2, l3, l4, l5, l6, d, g = params = sp.symbols(
'c1, c2, c3, c4, c5, c6, m1, m2, m3, m4, m5, m6, J1, J2, J3, J4, J5, J6, l1, l2, l3, l4, l5, l6, d, g')
tau1, tau2, tau3, tau4, tau5, tau6 = ttau = st.symb_vector("tau1, tau2, tau3, tau4, tau5, tau6 ")
# unit vectors
ex = sp.Matrix([1, 0])
ey = sp.Matrix([0, 1])
# coordinates of centers of mass and joints
# left
G0 = 0 * ex ##:
C1 = G0 + Rz(q1) * ex * c1 ##:
G1 = G0 + Rz(q1) * ex * l1 ##:
C2 = G1 + Rz(q1 + p1) * ex * c2 ##:
G2 = G1 + Rz(q1 + p1) * ex * l2 ##:
C3 = G2 + Rz(q1 + p1 + s1) * ex * c3 ##:
G3 = G2 + Rz(q1 + p1 + s1) * ex * l3 ##:
# right
G6 = d * ex ##:
C6 = G6 + Rz(q2) * ex * c6 ##:
G5 = G6 + Rz(q2) * ex * l6 ##:
C5 = G5 + Rz(q2 + p2) * ex * c5 ##:
G4 = G5 + Rz(q2 + p2) * ex * l5 ##:
C4 = G4 + Rz(q2 + p2 + s2) * ex * c4 ##:
G3b = G4 + Rz(q2 + p2 + s2) * ex * l4 ##:
# time derivatives of centers of mass
Sd1, Sd2, Sd3, Sd4, Sd5, Sd6 = st.col_split(st.time_deriv(st.col_stack(C1, C2, C3, C4, C5, C6), ttheta))
# Kinetic Energy (note that angles are relative)
T_rot = (J1 * qdot1 ** 2) / 2 + (J2 * (qdot1 + pdot1) ** 2) / 2 + (J3 * (qdot1 + pdot1 + sdot1) ** 2) / 2 + \
(J4 * (qdot2 + pdot2 + sdot2) ** 2) / 2 + (J5 * (qdot2 + pdot2) ** 2) / 2 + (J6 * qdot2 ** 2) / 2
T_trans = (
m1 * Sd1.T * Sd1 + m2 * Sd2.T * Sd2 + m3 * Sd3.T * Sd3 + m4 * Sd4.T * Sd4 + m5 * Sd5.T * Sd5 + m6 * Sd6.T * Sd6) / 2
T = T_rot + T_trans[0]
# Potential Energy
V = m1 * g * C1[1] + m2 * g * C2[1] + m3 * g * C3[1] + m4 * g * C4[1] + m5 * g * C5[1] + m6 * g * C6[1]
parameter_values = list(dict(c1=0.4 / 2, c2=0.42 / 2, c3=0.55 / 2, c4=0.55 / 2, c5=0.42 / 2, c6=0.4 / 2,
m1=6.0, m2=12.0, m3=39.6, m4=39.6, m5=12.0, m6=6.0,
J1=(6 * 0.4 ** 2) / 12, J2=(12 * 0.42 ** 2) / 12, J3=(39.6 * 0.55 ** 2) / 12,
J4=(39.6 * 0.55 ** 2) / 12, J5=(12 * 0.42 ** 2) / 12, J6=(6 * 0.4 ** 2) / 12,
l1=0.4, l2=0.42, l3=0.55, l4=0.55, l5=0.42, l6=0.4, d=0.26, g=9.81).items())
external_forces = [tau1, tau2, tau3, tau4, tau5, tau6]
dir_of_this_file = os.path.dirname(os.path.abspath(sys.modules.get(__name__).__file__))
fpath = os.path.join(dir_of_this_file, "7L-dae-2020-07-15.pcl")
if not os.path.isfile(fpath):
# if model is not present it could be regenerated
# however this might take long (approx. 20min)
mod = mt.generate_symbolic_model(T, V, ttheta, external_forces, constraints=[G3 - G3b], simplify=False)
mod.calc_state_eq(simplify=False)
mod.f_sympy = mod.f.subs(parameter_values)
mod.G_sympy = mod.g.subs(parameter_values)
with open(fpath, "wb") as pfile:
pickle.dump(mod, pfile)
else:
with open(fpath, "rb") as pfile:
mod = pickle.load(pfile)
# calculate DAE equations from symbolic model
dae = mod.calc_dae_eq(parameter_values)
dae.generate_eqns_funcs()
torso1_unit = Rz(q1 + p1 + s1) * ex
torso2_unit = Rz(q2 + p2 + s2) * ex
neck_length = 0.12
head_radius = 0.1
body_width = 15
neck_width = 15
H1 = G3 + neck_length * torso1_unit
H1r = G3 + (neck_length - head_radius) * torso1_unit
H2 = G3b + neck_length * torso2_unit
H2r = G3b + (neck_length - head_radius) * torso2_unit
vis = vt.Visualiser(ttheta, xlim=(-1.5, 1.5), ylim=(-.2, 2))
# get default colors and set them explicitly
# this prevents color changes in onion skin plot
default_colors = plt.get_cmap("tab10")
guy1_color = default_colors(0)
guy1_joint_color = "darkblue"
guy2_color = default_colors(1)
guy2_joint_color = "red"
guy1_head_fc = guy1_color # facecolor
guy1_head_ec = guy1_head_fc # edgecolor
guy2_head_fc = guy2_color # facecolor
guy2_head_ec = guy2_head_fc # edgecolor
# guy 1 body
vis.add_linkage(st.col_stack(G0, G1, G2, G3).subs(parameter_values),
color=guy1_color,
solid_capstyle='round',
lw=body_width,
ms=body_width,
mfc=guy1_joint_color)
# guy 1 neck
# vis.add_linkage(st.col_stack(G3, H1r).subs(parameter_values), color=head_color, solid_capstyle='round', lw=neck_width)
# guy 1 head
vis.add_disk(st.col_stack(H1, H1r).subs(parameter_values), fc=guy1_head_fc, ec=guy1_head_ec, plot_radius=False,
fill=True)
# guy 2 body
vis.add_linkage(st.col_stack(G6, G5, G4, G3b).subs(parameter_values),
color=guy2_color,
solid_capstyle='round',
lw=body_width,
ms=body_width,
mfc=guy2_joint_color)
# guy 2 neck
# vis.add_linkage(st.col_stack(G3b, H2r).subs(parameter_values), color=head_color, solid_capstyle='round', lw=neck_width)
# guy 2 head
vis.add_disk(st.col_stack(H2, H2r).subs(parameter_values), fc=guy2_head_fc, ec=guy2_head_ec, plot_radius=False,
fill=True)
eq_stat = mod.eqns.subz0(tthetadd).subz0(tthetad).subs(parameter_values)
# vector for tau and lambda together
ttau_symbols = sp.Matrix(mod.uu) ##:T
mmu = st.row_stack(ttau_symbols, mod.llmd) ##:T
# linear system of equations (and convert to function w.r.t. ttheta)
K0_expr = eq_stat.subz0(mmu) ##:i
K1_expr = eq_stat.jacobian(mmu) ##:i
K0_func = st.expr_to_func(ttheta, K0_expr)
K1_func = st.expr_to_func(ttheta, K1_expr, keep_shape=True)
def get_mu_stat_for_theta(ttheta_arg, rho=10):
# weighting matrix for mu
K0 = K0_func(*ttheta_arg)
K1 = K1_func(*ttheta_arg)
return solve_qlp(K0, K1, rho)
def solve_qlp(K0, K1, rho):
R_mu = npy.diag([1, 1, 1, rho, rho, rho, .1, .1])
n1, n2 = K1.shape
# construct the equation system for least squares with linear constraints
M1 = npy.column_stack((R_mu, K1.T))
M2 = npy.column_stack((K1, npy.zeros((n1, n1))))
M_coeff = npy.row_stack((M1, M2))
M_rhs = npy.concatenate((npy.zeros(n2), -K0))
mmu_stat = npy.linalg.solve(M_coeff, M_rhs)[:n2]
return mmu_stat
ttheta_start = npy.r_[0.9, 1.5, -1.9, 2.1, -2.175799453493845, 1.7471971159642905]
mmu_start = get_mu_stat_for_theta(ttheta_start)
connection_point_func = st.expr_to_func(ttheta, G3.subs(parameter_values))
cs_ttau = mpc.casidify(mod.uu, mod.uu)[0]
cs_llmd = mpc.casidify(mod.llmd, mod.llmd)[0]
controls_sp = mmu
controls_cs = cs.vertcat(cs_ttau, cs_llmd)
coords_cs, _ = mpc.casidify(ttheta, ttheta)
# parameters: 0: value of y_connection, 1: x_connection_last,
# 2: y_connection_last, 3: delta_r_max, 4: rho (penalty factor for 2nd persons torques),
# 5:11: ttheta_old[6], 11:17: ttheta:old2
#
P = SX.sym('P', 5 + 12)
rho = P[10]
# weightning of inputs
R = mpc.SX_diag_matrix((1, 1, 1, rho, rho, rho, 0.1, 0.1))
# Construction of Constraints
g1 = [] # constraints vector (system dynamics)
g2 = [] # inequality-constraints
closed_chain_constraint, _ = mpc.casidify(mod.dae.constraints, ttheta, cs_vars=coords_cs)
connection_position, _ = mpc.casidify(list(G3.subs(parameter_values)), ttheta, cs_vars=coords_cs) ##:i
connection_y_value, _ = mpc.casidify([G3[1].subs(parameter_values)], ttheta, cs_vars=coords_cs) ##:i
stationary_eqns, _, _ = mpc.casidify(eq_stat, ttheta, controls_sp, cs_vars=(coords_cs, controls_cs)) ##:i
g1.extend(mpc.unpack(stationary_eqns))
g1.extend(mpc.unpack(closed_chain_constraint))
# force the connecting joint to a given hight (which will be provided later)
g1.append(connection_y_value - P[0])
ng1 = len(g1)
# squared distance from the last reference should be smaller than P[3] (delta_r_max):
# this will be a restriction between -inf and 0
r = connection_position - P[1:3]
g2.append(r.T @ r - P[3])
# change of angles should be smaller than a given bound (P[5:11] are the old coords)
coords_old = P[5:11]
coords_old2 = P[11:17]
pseudo_vel = (coords_cs - coords_old) / 1
pseudo_acc = (coords_cs - 2 * coords_old + coords_old2) / 1
g2.extend(mpc.unpack(pseudo_vel))
g2.extend(mpc.unpack(pseudo_acc))
g_all = mpc.seq_to_SX_matrix(g1 + g2)
### Construction of objective Function
obj = controls_cs.T @ R @ controls_cs + 1e5 * pseudo_acc.T @ pseudo_acc + 0.3e6 * pseudo_vel.T @ pseudo_vel
OPT_variables = cs.vertcat(coords_cs, controls_cs)
# for debugging
g_all_cs_func = cs.Function("g_all_cs_func", (OPT_variables, P), (g_all,))
nlp_prob = dict(f=obj, x=OPT_variables, g=g_all, p=P)
ipopt_settings = dict(max_iter=5000, print_level=0,
acceptable_tol=1e-8, acceptable_obj_change_tol=1e-6)
opts = dict(print_time=False, ipopt=ipopt_settings)
xx_guess = npy.r_[ttheta_start, mmu_start]
# note: g1 contains the equality constraints (system dynamics) (lower bound = upper bound)
delta_phi = .05
d_delta_phi = .02
eps = 1e-9
lbg = npy.r_[[-eps] * ng1 + [-inf] + [-delta_phi] * n, [-d_delta_phi] * n]
ubg = npy.r_[[eps] * ng1 + [0] + [delta_phi] * n, [d_delta_phi] * n]
# ubx = [inf]*OPT_variables.shape[0]##:
# lower and upper bounds for decision variables:
# lbx = [-inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf]*N1 + [tau1_min, tau4_min, -inf, -inf]*N
# ubx = [inf, inf, inf, inf, inf, inf, inf, inf]*N1 + [tau1_max, tau4_max, inf, inf]*N
rho = 3
P_init = npy.r_[connection_point_func(*ttheta_start)[1],
connection_point_func(*ttheta_start), 0.01, rho, ttheta_start, ttheta_start]
args = dict(lbx=-inf, ubx=inf, lbg=lbg, ubg=ubg, # unconstrained optimization
p=P_init, # initial and final state
x0=xx_guess # initial guess
)
solver = cs.nlpsol("solver", "ipopt", nlp_prob, opts)
sol = solver(**args)
global_vars = ipydex.Container(old_sol=xx_guess, old_sol2=xx_guess)
def get_optimal_equilibrium(y_value, rho=3):
ttheta_old = global_vars.old_sol[:n]
ttheta_old2 = global_vars.old_sol2[:n]
opt_prob_params = npy.r_[y_value, connection_point_func(*ttheta_old),
0.01, rho, ttheta_old, ttheta_old2]
args.update(dict(p=opt_prob_params, x0=global_vars.old_sol))
sol = solver(**args)
stats = solver.stats()
if not stats['success']:
raise ValueError(stats["return_status"])
XX = sol["x"].full().squeeze()
# save the last two results
global_vars.old_sol2 = global_vars.old_sol
global_vars.old_sol = XX
return XX
y_start = connection_point_func(*ttheta_start)[1]
N = 100
y_end = 1.36
y_func = st.expr_to_func(t, st.condition_poly(t, (0, y_start, 0, 0), (1, y_end, 0, 0)))
def get_qs_trajectory(rho):
pseudo_time = npy.linspace(0, 1, N)
yy_connection = y_func(pseudo_time)
# reset the initial guess
global_vars.old_sol = xx_guess
global_vars.old_sol2 = xx_guess
XX_list = []
for i, y_value in enumerate(yy_connection):
# print(i, y_value)
XX_list.append(get_optimal_equilibrium(y_value, rho=rho))
XX = npy.array(XX_list)
return XX
rho = 30
XX = get_qs_trajectory(rho=rho)
def smooth_time_scaling(Tend, N, phase_fraction=.5):
"""
:param Tend:
:param N:
:param phase_fraction: fraction of Tend for smooth initial and end phase
"""
T0 = 0
T1 = Tend * phase_fraction
y0 = 0
y1 = 1
# for initial phase
poly1 = st.condition_poly(t, (T0, y0, 0, 0), (T1, y1, 0, 0))
# for end phase
poly2 = poly1.subs(t, Tend - t)
# there should be a phase in the middle with constant slope
deriv_transition = st.piece_wise((y0, t < T0), (poly1, t < T1), (y1, t < Tend - T1),
(poly2, t < Tend), (y0, True))
scaling = sp.integrate(deriv_transition, (t, T0, Tend))
time_transition = sp.integrate(deriv_transition * N / scaling, t)
# deriv_transition_func = st.expr_to_func(t, full_transition)
time_transition_func = st.expr_to_func(t, time_transition)
deriv_func = st.expr_to_func(t, deriv_transition * N / scaling)
deriv_func2 = st.expr_to_func(t, deriv_transition.diff(t) * N / scaling)
C = ipydex.Container(fetch_locals=True)
return C
N = XX.shape[0]
Tend = 4
res = smooth_time_scaling(Tend, N)
def get_derivatives(XX, time_scaling, res=100):
"""
:param XX: Nxm array
:param time_scaling: container for time scaling
:param res: time resolution of the returned arrays
"""
N = XX.shape[0]
Tend = time_scaling.Tend
assert npy.isclose(time_scaling.time_transition_func([0, Tend])[-1], N)
tt = npy.linspace(time_scaling.T0, time_scaling.Tend, res)
NN = npy.arange(N)
# high_resolution version of index arry
NN2 = npy.linspace(0, N, res, endpoint=False)
# time-scaled verion of index-array
NN3 = time_scaling.time_transition_func(tt)
NN3d = time_scaling.deriv_func(tt)
NN3dd = time_scaling.deriv_func2(tt)
XX_num, XXd_num, XXdd_num = [], [], []
# iterate over every column
for col in XX.T:
spl = splrep(NN, col)
# function value and derivatives
XX_num.append(splev(NN3, spl))
XXd_num.append(splev(NN3, spl, der=1))
XXdd_num.append(splev(NN3, spl, der=2))
XX_num = npy.array(XX_num).T
XXd_num = npy.array(XXd_num).T
XXdd_num = npy.array(XXdd_num).T
NN3d_bc = npy.broadcast_to(NN3d, XX_num.T.shape).T
NN3dd_bc = npy.broadcast_to(NN3dd, XX_num.T.shape).T
XXd_n = XXd_num * NN3d_bc
# apply chain rule
XXdd_n = XXdd_num * NN3d_bc ** 2 + XXd_num * NN3dd_bc
C = ipydex.Container(fetch_locals=True)
return C
C = XX_derivs = get_derivatives(XX[:, :], time_scaling=res)
expr = mod.eqns.subz0(mod.uu, mod.llmd).subs(parameter_values)
dynterm_func = st.expr_to_func(ttheta_all, expr)
def get_torques(dyn_term_func, XX_derivs, static1=False, static2=False):
ttheta_num_all = npy.c_[XX_derivs.XX_num[:, :n], XX_derivs.XXd_n[:, :n], XX_derivs.XXdd_n[:, :n]] ##:S
if static1:
# set velocities to 0
ttheta_num_all[:, n:2 * n] = 0
if static2:
# set accelerations to 0
ttheta_num_all[:, 2 * n:] = 0
res = dynterm_func(*ttheta_num_all.T)
return res
lhs_static = get_torques(dynterm_func, XX_derivs, static1=True, static2=True) ##:i
lhs_dynamic = get_torques(dynterm_func, XX_derivs, static2=False) ##:i
mmu_stat_list = []
for L_k_stat, L_k_dyn, ttheta_k in zip(lhs_static, lhs_dynamic, XX_derivs.XX_num[:, :n]):
K1_k = K1_func(*ttheta_k)
mmu_stat_k = solve_qlp(L_k_stat, K1_k, rho)
mmu_stat_list.append(mmu_stat_k)
mmu_stat_all = npy.array(mmu_stat_list)
solution_data = SolutionData()
solution_data.tt = XX_derivs.tt
solution_data.xx = XX_derivs.XX_num
solution_data.mmu = mmu_stat_all
solution_data.vis = vis
save_plot(problem_spec, solution_data)
return solution_data
tau1 = sp.Symbol("tau1")
#Einheitsvektoren
ex = sp.Matrix([1, 0])
ey = sp.Matrix([0, 1])
# Koordinaten der Schwerpunkte und Gelenke
S0 = ex * q1 # Schwerpunkt Wagen
G0 = S0 # Gelenk zwischen Wagen und Pendel 1
# Schwerpunkte des Pendels (Pendel zeigt für kleine Winkel nach oben)
S1 = G0 + mt.Rz(p1) * ey * s1
# Zeitableitungen der Schwerpunktskoordinaten
Sd0, Sd1 = st.col_split(st.time_deriv(st.col_stack(S0, S1), ttheta))
# Energie
T_rot = 0
T_trans = (m0 * Sd0.T * Sd0 + m1 * Sd1.T * Sd1) / 2
T = T_rot + T_trans[0]
V = m1 * g * S1[1]
mod = mt.generate_symbolic_model(T, V, ttheta, [0, tau1])
# Zustandsraummodell
mod.calc_state_eq(simplify=True)
x_dot = mod.f + mod.g * tau1