ref_state = {("c_capture", ((), )): 0.52}
c = NmpcGen(d_mod=bfb_dae,
u=u,
states=states,
ref_state=ref_state,
u_bounds=u_bounds)
c.ss.dref = snap
c.load_iguess_ss()
c.solve_ss()
c.load_d_s(c.d1)
c.solve_d(c.d1)
c.update_state_real() # update the current state
c.find_target_ss()
c.create_nmpc()
c.update_targets_nmpc()
c.compute_QR_nmpc(n=-1)
c.new_weights_olnmpc(1000, 1e+06)
# q_cov = {}
# for i in range(1, nfet):
# for j in range(1, ntrays + 1):
# q_cov[("x", (j,)), ("x", (j,)), i] = 1e-05
# q_cov[("M", (j,)), ("M", (j,)), i] = 0.5
#
# m_cov = {}
# for i in range(1, nfet + 1):
# for j in range(1, ntrays + 1):
# m_cov[("T", (j,)), ("T", (j,)), i] = 6.25e-2
tfe = [i for i in range(1, nfet + 1)]
c = NmpcGen(d_mod=DistDiehlNegrete,
u=u,
states=states,
ref_state=ref_state,
u_bounds=u_bounds)
c.load_iguess_ss()
c.solve_ss()
c.load_d_s(c.d1)
c.solve_d(c.d1)
c.update_state_real() # update the current state
c.find_target_ss()
c.create_nmpc()
c.update_targets_nmpc()
c.compute_QR_nmpc(n=-1)
c.new_weights_olnmpc(10000, 1e+06)
c.d1.create_bounds()
c.create_predictor()
c.predictor_step(c.d1, "real")
q_cov = {}
for j in range(1, ntrays + 1):
q_cov[("x", (j, ))] = 1e-05
q_cov[("M", (j, ))] = 1
c.make_noisy(q_cov)