def main():
states = ["Hgc", "Nsc", "Hsc", "Hge", "Nse", "Hse"]
x_noisy = ["Hgc", "Nsc", "Hsc", "Hge", "Nse", "Hse"]
u = ["u1"]
u_bounds = {"u1": (162.183495794 * 0.0005, 162.183495794 * 10000)}
ref_state = {("c_capture", ((), )): 0.50}
nfe_mhe = 10
y = ["Tgb", "vg"]
nfet = 10
ncpx = 3
nfex = 5
tfe = [i for i in range(1, nfe_mhe + 1)]
lfe = [i for i in range(1, nfex + 1)]
lcp = [i for i in range(1, ncpx + 1)]
lc = ['c', 'h', 'n']
y_vars = {
"Tgb": [i for i in itertools.product(lfe, lcp)],
"vg": [i for i in itertools.product(lfe, lcp)]
}
# x_vars = dict()
x_vars = {
# "Nge": [i for i in itertools.product(lfe, lcp, lc)],
# "Hge": [i for i in itertools.product(lfe, lcp)],
"Nsc": [i for i in itertools.product(lfe, lcp, lc)],
"Hsc": [i for i in itertools.product(lfe, lcp)],
"Nse": [i for i in itertools.product(lfe, lcp, lc)],
"Hse": [i for i in itertools.product(lfe, lcp)],
"Hgc": [i for i in itertools.product(lfe, lcp)],
"Hge": [i for i in itertools.product(lfe, lcp)],
# "mom": [i for i in itertools.product(lfe, lcp)]
}
# States -- (5 * 3 + 6) * fe_x * cp_x.
# For fe_x = 5 and cp_x = 3 we will have 315 differential-states.
e = MheGen(bfb_dae,
800 / nfe_mhe,
states,
u,
x_noisy,
x_vars,
y,
y_vars,
nfe_tmhe=nfe_mhe,
ncp_tmhe=1,
nfe_tnmpc=nfe_mhe,
ncp_tnmpc=1,
ref_state=ref_state,
u_bounds=u_bounds,
nfe_t=5,
ncp_t=1,
k_aug_executable="/home/dav0/k2/KKT_matrix/src/k_aug/k_aug")
# 10 fe & _t=1000 definitely degenerate
# 10 fe & _t=900 definitely degenerate
# 10 fe & _t=120 sort-of degenerate
# 10 fe & _t=50 sort-of degenerate
# 10 fe & _t=50 eventually sort-of degenerate
# 10 fe & _t=1 eventually sort-of degenerate
e.load_solfile(e.SteadyRef, "ref_ss.sol") #: Loads solfile snap
results = e.ipopt.solve(e.SteadyRef,
tee=True,
load_solutions=True,
report_timing=True)
if results.solver.termination_condition != TerminationCondition.optimal:
print("Oh no!")
sys.exit()
e.get_state_vars(skip_solve=True)
e.SteadyRef.report_zL(filename="mult_ss")
e.load_d_s(e.PlantSample)
e.PlantSample.create_bounds()
e.solve_dyn(e.PlantSample)
q_cov = {}
for i in tfe:
for j in itertools.product(lfe, lcp, lc):
q_cov[("Nse", j), ("Nse", j), i] = 7525.81478168 * 0.005
q_cov[("Nsc", j), ("Nsc", j), i] = 117.650089456 * 0.005
# q_cov[("Nse", j), ("Nse", j), i] = 735.706082714 * 0.005
for i in tfe:
for j in itertools.product(lfe, lcp):
# q_cov[("Hge", j), ("Hge", j), i] = 2194.25390583 * 0.005
q_cov[("Hse", j), ("Hse", j), i] = 731143.716603 * 0.005
q_cov[("Hsc", j), ("Hsc", j), i] = 16668.3312216 * 0.005
q_cov[("Hge", j), ("Hge", j), i] = 2166.86838591 * 0.005
q_cov[("Hgc", j), ("Hgc", j), i] = 47.7911012193 * 0.005
# q_cov[("mom", j), ("mom", j), i] = 1.14042251669 * 0.005
# for i in lfe:
# for j in [(1,1, 'c'), (5,3, 'c')]:
# m_cov[("yb", j), ("yb", j), i] = 1e-04
u_cov = {}
for i in [i for i in range(1, nfe_mhe + 1)]:
u_cov["u1", i] = 162.183495794 * 0.005
m_cov = {}
for i in tfe:
for j in itertools.product(lfe, lcp):
m_cov[("Tgb", j), ("Tgb", j), i] = 40 * 0.005
m_cov[("vg", j), ("vg", j), i] = 0.902649386907 * 0.005
e.set_covariance_meas(m_cov)
e.set_covariance_disturb(q_cov)
e.set_covariance_u(u_cov)
e.create_rh_sfx() #: Reduced hessian computation
# dum = e.d_mod(1, 6, _t=e.hi_t)
# e.PlantSample.display(filename="schwer0.txt")
# e.load_iguess_single(e.PlantSample, dum, src_fe=1, tgt_fe=1)
# dum.display(filename="schwer1.txt")
# e.load_init_state_gen(dum, src_kind="mod", ref=e.PlantSample, fe=1)
# e.init_step_mhe(dum, e.nfe_t)
# e.solve_dyn(dum)
# dum.display(filename="schwer2.txt")
e.init_lsmhe_prep(e.PlantSample)
e.shift_mhe()
e.init_step_mhe()
e.solve_dyn(e.lsmhe,
skip_update=False,
max_cpu_time=600,
ma57_pre_alloc=5,
tag="lsmhe") #: Pre-loaded mhe solve
e.check_active_bound_noisy()
e.load_covariance_prior()
e.set_state_covariance()
e.regen_objective_fun() #: Regen erate the obj fun
e.deact_icc_mhe() #: Remove the initial conditions
e.set_prior_state_from_prior_mhe() #: Update prior-state
e.find_target_ss() #: Compute target-steady state (beforehand)
#: Create NMPC
e.create_nmpc()
e.update_targets_nmpc()
e.compute_QR_nmpc(n=-1)
e.new_weights_olnmpc(10000, 1e+06)
e.solve_dyn(e.PlantSample, stop_if_nopt=True)
isnap = [i * 50 for i in range(1, 25)]
for i in range(1, 1200):
if i in isnap:
keepsolve = True
wantparams = True
else:
keepsolve = False
wantparams = False
if i == 400:
ref_state = {("c_capture", ((), )): 0.63}
e.change_setpoint(ref_state=ref_state,
keepsolve=True,
wantparams=True)
e.compute_QR_nmpc(n=-1)
e.new_weights_olnmpc(10000, 1e+06)
elif i == 800:
ref_state = {("c_capture", ((), )): 0.5}
e.change_setpoint(ref_state=ref_state,
keepsolve=True,
wantparams=True)
e.compute_QR_nmpc(n=-1)
e.new_weights_olnmpc(10000, 1e+06)
e.solve_dyn(e.PlantSample,
stop_if_nopt=True,
tag="plant",
keepsolve=keepsolve,
wantparams=wantparams)
e.PlantSample.hi_t.display()
e.update_state_real() # update the current state
e.update_soi_sp_nmpc()
#
e.update_noise_meas(m_cov)
e.patch_input_mhe("mod",
src=e.PlantSample) #: The inputs must coincide
#
e.patch_meas_mhe(e.PlantSample, noisy=True) #: Get the measurement
e.compute_y_offset()
#
e.init_step_mhe() # Initialize next time-slot
#
stat = e.solve_dyn(e.lsmhe,
skip_update=False,
iter_max=500,
jacobian_regularization_value=1e-04,
max_cpu_time=600,
tag="lsmhe",
keepsolve=keepsolve,
wantparams=wantparams)
e.lsmhe.write_nl(name="failed_mhe1.nl")
# e.lsmhe.hi_t.display()
e.lsmhe.report_zL()
if stat == 1:
stat = e.solve_dyn(e.lsmhe,
skip_update=True,
iter_max=250,
stop_if_nopt=True,
jacobian_regularization_value=1e-02,
linear_scaling_on_demand=True,
tag="lsmhe")
if stat != 0:
e.lsmhe.write_nl(name="bad_mhe.nl")
sys.exit()
e.update_state_mhe()
# # Prior-Covariance stuff
e.check_active_bound_noisy()
e.load_covariance_prior()
e.set_state_covariance()
e.regen_objective_fun()
# # Update prior-state
e.set_prior_state_from_prior_mhe()
#
e.print_r_mhe()
e.print_r_dyn()
#
e.shift_mhe()
e.shift_measurement_input_mhe()
e.initialize_olnmpc(e.PlantSample, "estimated")
e.load_init_state_nmpc(src_kind="state_dict", state_dict="estimated")
stat_nmpc = e.solve_dyn(e.olnmpc,
skip_update=False,
max_cpu_time=300,
jacobian_regularization_value=1e-04,
tag="olnmpc",
keepsolve=keepsolve,
wantparams=wantparams)
if stat_nmpc != 0:
e.olnmpc.write_nl(name="bad.nl")
e.solve_dyn(e.olnmpc,
skip_update=False,
max_cpu_time=300,
jacobian_regularization_value=1e-04,
tag="olnmpc")
if stat != 0:
e.lsmhe.write_nl(name="bad_mhe.nl")
sys.exit()
e.update_u(e.olnmpc)
e.print_r_nmpc()
#
e.cycleSamPlant(plant_step=True)
e.plant_uinject(e.PlantSample, src_kind="dict", skip_homotopy=True)
u_cov["u1", i] = 162.183495794 * 0.005
m_cov = {}
for i in tfe:
for j in itertools.product(lfe, lcp):
m_cov[("Tgb", j), ("Tgb", j), i] = 40 * 0.005
m_cov[("vg", j), ("vg", j), i] = 0.902649386907 * 0.005
e.set_covariance_meas(m_cov)
e.set_covariance_disturb(q_cov)
e.set_covariance_u(u_cov)
e.create_rh_sfx() #: Reduced hessian computation
# Preparation phase
e.init_lsmhe_prep(e.d1, update=True)
e.lsmhe.display(filename="mhe0")
e.shift_mhe()
dum = e.d_mod(1, e.ncp_t, _t=e.hi_t)
e.init_step_mhe(dum, e.nfe_t)
e.solve_d(e.lsmhe, skip_update=False,
max_cpu_time=600) #: Pre-loaded mhe solve
e.create_rh_sfx() #: Reduced hessian computation
e.check_active_bound_noisy()
e.load_covariance_prior()
e.set_state_covariance()
e.regen_objective_fun() #: Regen erate the obj fun
e.deact_icc_mhe() #: Remove the initial conditions
def main():
u_weight = 1E-04
states = ["Hgc", "Nsc", "Hsc", "Hge", "Nse", "Hse"]
x_noisy = ["Hgc", "Nsc", "Hsc", "Hge", "Nse", "Hse"]
u = ["u1"]
u_bounds = {"u1":(162.183495794 * 0.0005, 162.183495794 * 10000)}
ref_state = {("c_capture", ((),)): 0.50}
nfe_mhe = 20
y = ["Tgb", "vg"]
nfet = 10
ncpx = 3
nfex = 5
tfe = [i for i in range(0, nfe_mhe)]
lfe = [i for i in range(0, nfex)]
lcp = [i for i in range(1, ncpx + 1)]
lc = ['c', 'h', 'n']
y_vars = {
"Tgb": [i for i in itertools.product(lfe, lcp)],
"vg": [i for i in itertools.product(lfe, lcp)]
}
# x_vars = dict()
x_vars = {
# "Nge": [i for i in itertools.product(lfe, lcp, lc)],
# "Hge": [i for i in itertools.product(lfe, lcp)],
"Nsc": [i for i in itertools.product(lfe, lcp, lc)],
"Hsc": [i for i in itertools.product(lfe, lcp)],
"Nse": [i for i in itertools.product(lfe, lcp, lc)],
"Hse": [i for i in itertools.product(lfe, lcp)],
"Hgc": [i for i in itertools.product(lfe, lcp)],
"Hge": [i for i in itertools.product(lfe, lcp)],
# "mom": [i for i in itertools.product(lfe, lcp)]
}
# States -- (5 * 3 + 6) * fe_x * cp_x.
# For fe_x = 5 and cp_x = 3 we will have 315 differential-states.
#: 1600 was proven to be solveable
e = MheGen(bfb_dae, 1600/nfe_mhe, states, u, x_noisy, x_vars, y, y_vars,
nfe_tmhe=nfe_mhe, ncp_tmhe=1,
nfe_tnmpc=nfe_mhe, ncp_tnmpc=1,
ref_state=ref_state, u_bounds=u_bounds,
nfe_t=5, ncp_t=1,
k_aug_executable="/home/dav0/k_aug/src/k_aug/k_aug",
dot_driver_executable="/home/dav0/k_aug/src/k_aug/dot_driver/dot_driver"
)
e.load_solfile(e.SteadyRef, "ref_ss.sol") #: Loads solfile snap
results = e.ipopt.solve(e.SteadyRef, tee=True, load_solutions=True, report_timing=True)
e.get_state_vars(skip_solve=True)
e.SteadyRef.report_zL(filename="mult_ss")
e.load_d_s(e.PlantSample)
e.PlantSample.create_bounds()
e.solve_dyn(e.PlantSample)
q_cov = {}
for i in tfe:
for j in itertools.product(lfe, lcp, lc):
q_cov[("Nse", j), ("Nse", j), i] = 7525.81478168 * 0.005
q_cov[("Nsc", j), ("Nsc", j), i] = 117.650089456 * 0.005
# q_cov[("Nse", j), ("Nse", j), i] = 735.706082714 * 0.005
for i in tfe:
for j in itertools.product(lfe, lcp):
# q_cov[("Hge", j), ("Hge", j), i] = 2194.25390583 * 0.005
q_cov[("Hse", j), ("Hse", j), i] = 731143.716603 * 0.005
q_cov[("Hsc", j), ("Hsc", j), i] = 16668.3312216 * 0.005
q_cov[("Hge", j), ("Hge", j), i] = 2166.86838591 * 0.005
q_cov[("Hgc", j), ("Hgc", j), i] = 47.7911012193 * 0.005
# q_cov[("mom", j), ("mom", j), i] = 1.14042251669 * 0.005
# for i in lfe:
# for j in [(1,1, 'c'), (5,3, 'c')]:
# m_cov[("yb", j), ("yb", j), i] = 1e-04
u_cov = {}
for i in [i for i in range(0, nfe_mhe)]:
u_cov["u1", i] = 162.183495794 * 0.005
m_cov = {}
for i in tfe:
for j in itertools.product(lfe, lcp):
m_cov[("Tgb", j), ("Tgb", j), i] = 40 * 0.005
m_cov[("vg", j), ("vg", j), i] = 0.902649386907 * 0.005
e.set_covariance_meas(m_cov)
e.set_covariance_disturb(q_cov)
e.set_covariance_u(u_cov)
e.create_rh_sfx() #: Reduced hessian computation
e.init_lsmhe_prep(e.PlantSample)
e.shift_mhe()
e.init_step_mhe()
e.solve_dyn(e.lsmhe,
skip_update=False,
max_cpu_time=600,
ma57_pre_alloc=5, tag="lsmhe") #: Pre-loaded mhe solve
e.check_active_bound_noisy()
e.load_covariance_prior()
e.set_state_covariance()
e.regen_objective_fun() #: Regen erate the obj fun
e.deact_icc_mhe() #: Remove the initial conditions
e.set_prior_state_from_prior_mhe() #: Update prior-state
e.find_target_ss() #: Compute target-steady state (beforehand)
#: Create NMPC
e.create_nmpc()
e.create_suffixes_nmpc()
e.update_targets_nmpc()
e.compute_QR_nmpc(n=-1)
e.new_weights_olnmpc(u_weight, 1e+06)
e.solve_dyn(e.PlantSample, stop_if_nopt=True)
# isnap = [i*50 for i in range(1, 25)]
isnap = [i*25 for i in range(2, 30)]
j = 1
cw_u = 1e+06
for i in range(1, 600):
if i in isnap:
keepsolve=False
wantparams=False
else:
keepsolve=False
wantparams=False
if i == 200:
j = 1
ref_state = {("c_capture", ((),)): 0.63}
e.change_setpoint(ref_state=ref_state, keepsolve=True, wantparams=True, tag="sp")
e.compute_QR_nmpc(n=-1)
e.new_weights_olnmpc(u_weight, 1e+06)
elif i == 400:
j = 1
ref_state = {("c_capture", ((),)): 0.5}
e.change_setpoint(ref_state=ref_state, keepsolve=True, wantparams=True, tag="sp")
e.compute_QR_nmpc(n=-1)
e.new_weights_olnmpc(u_weight, 1e+06)
stat = e.solve_dyn(e.PlantSample, stop_if_nopt=False, tag="plant", keepsolve=keepsolve, wantparams=wantparams)
if stat == 1:
e.noisy_plant_manager(action="remove")
e.solve_dyn(e.PlantSample, stop_if_nopt=True, tag="plant", keepsolve=keepsolve,
wantparams=wantparams) #: Try again (without noise)
e.update_state_real() # update the current state
e.update_soi_sp_nmpc()
e.update_noise_meas(m_cov) #: the noise it is not being added
e.update_measurement()
e.compute_y_offset() #: Get the offset for y
e.preparation_phase_mhe(as_strategy=False)
stat = e.solve_dyn(e.lsmhe,
skip_update=False, iter_max=500,
jacobian_regularization_value=1e-04,
max_cpu_time=600, tag="lsmhe", keepsolve=keepsolve, wantparams=wantparams)
if stat == 1: #: Try again
e.lsmhe.write_nl(name="bad_mhe.nl")
stat = e.solve_dyn(e.lsmhe,
skip_update=True,
max_cpu_time=600,
stop_if_nopt=True,
jacobian_regularization_value=1e-02,
linear_scaling_on_demand=True, tag="lsmhe")
if stat != 0:
sys.exit()
e.update_state_mhe() #: get the state from mhe
#: At this point computing and loading the Covariance is not going to affect the sens update of MHE
e.prior_phase()
#
e.print_r_mhe()
e.print_r_dyn()
#
e.preparation_phase_nmpc(as_strategy=False, make_prediction=False)
# e.initialize_olnmpc(e.PlantSample, "estimated")
# e.load_init_state_nmpc(src_kind="state_dict", state_dict="estimated")
stat_nmpc = e.solve_dyn(e.olnmpc, skip_update=False, max_cpu_time=300,
jacobian_regularization_value=1e-04, tag="olnmpc",
keepsolve=keepsolve, wantparams=wantparams)
if stat_nmpc != 0:
e.olnmpc.write_nl(name="bad.nl")
e.solve_dyn(e.olnmpc, skip_update=False,
max_cpu_time=600, jacobian_regularization_value=1e-04, linear_scaling_on_demand=True,
tag="olnmpc")
if stat != 0:
sys.exit()
e.update_u(e.olnmpc) #: Get the resulting input for k+1
e.print_r_nmpc()
#
e.cycleSamPlant(plant_step=True)
e.plant_uinject(e.PlantSample, src_kind="dict", skip_homotopy=True)
e.noisy_plant_manager(sigma=0.001, action="apply", update_level=True)
j += 1
m_cov[("vg", j), ("vg", j), i] = 0.902649386907 * 0.005
e.set_covariance_meas(m_cov)
e.set_covariance_disturb(q_cov)
e.set_covariance_u(u_cov)
e.create_rh_sfx() #: Reduced hessian computation
# dum = e.d_mod(1, 6, _t=e.hi_t)
# e.PlantSample.display(filename="schwer0.txt")
# e.load_iguess_single(e.PlantSample, dum, src_fe=1, tgt_fe=1)
# dum.display(filename="schwer1.txt")
# e.load_init_state_gen(dum, src_kind="mod", ref=e.PlantSample, fe=1)
# e.init_step_mhe(dum, e.nfe_t)
# e.solve_dyn(dum)
# dum.display(filename="schwer2.txt")
e.init_lsmhe_prep(e.PlantSample)
e.shift_mhe()
e.init_step_mhe()
e.solve_dyn(e.lsmhe, skip_update=False, max_cpu_time=600,
ma57_pre_alloc=5) #: Pre-loaded mhe solve
e.check_active_bound_noisy()
e.load_covariance_prior()
e.set_state_covariance()
e.regen_objective_fun() #: Regen erate the obj fun
e.deact_icc_mhe() #: Remove the initial conditions
e.set_prior_state_from_prior_mhe() #: Update prior-state
e.find_target_ss() #: Compute target-steady state (beforehand)