# c.load_covariance_prior() # c.set_state_covariance() # c.regen_objective_fun() # Update prior-state # c.set_prior_state_from_prior_mhe() # # c.print_r_mhe() # Compute the controls # c.initialize_olnmpc(c.d1, "real") # c.load_init_state_nmpc(src_kind="state_dict", state_dict="real") # for good measure # # if i == 5: # # with open("somefilc.txt", "w") as f: # # c.olnmpc.R_nmpc.display(ostream=f) # # c.olnmpc.Q_nmpc.display(ostream=f) # # f.close() # # stat_nmpc = c.solve_d(c.olnmpc, skip_update=False) # if stat_nmpc != 0: # stat_nmpc = c.solve_d(c.olnmpc, stop_if_nopt=True, skip_update=False, iter_max=300) # c.olnmpc.write_nl() # c.update_u(c.olnmpc) c.print_r_nmpc() # c.shift_mhe() # c.shift_measurement_input_mhe() c.curr_u["u1"] = value(c.d1.u1[1]) * 1.5 print(value(c.d1.Gb[1, 1, 1, 1]), value(c.d1.Tgb[1, 1, 1, 1])) c.cycle_ics(plant_step=True) c.plant_input_gen(c.d1, src_kind="dict") # c.d1.u1[1] = value(c.d1.u1[1]) * 0.99
# c.solve_k_aug_nmpc() # c.olnmpc.write_nl() i = 1 k = 1 while k != 0: if i == 1: k = c.solve_d(c.olnmpc, max_cpu_time=60 * i) else: k = c.solve_d(c.olnmpc, max_cpu_time=60 * i, warm_start=True) c.olnmpc.ipopt_zL_in.update(c.olnmpc.ipopt_zL_out) c.olnmpc.ipopt_zU_in.update(c.olnmpc.ipopt_zU_out) i += 1 c.initialize_olnmpc(c.d1) c.olnmpc.display(filename="somefile1.txt") c.solve_d(c.olnmpc, max_cpu_time=60 * 60) c.update_u(src=c.olnmpc) c.cycle_ics() c.plant_input_gen(c.d1, src_kind="dict") c.solve_d(c.d1) c.update_state_real() c.initialize_olnmpc(c.olnmpc, fe=5) c.solve_d(c.olnmpc, max_cpu_time=60 * 60) c.create_predictor() c.predictor_step(c.d1) c.update_state_predicted() c.update_u(src=c.olnmpc) c.update_soi_sp_nmpc() # c.print_r_nmpc()