# Objective function
Q = sparse.diags([1., 0.1])
QN = Q
R = 0.1 * sparse.eye(1)
# Prediction horizon
N = 10
#%% 1. Pysim Model import
# Powertrain model import
kona_power = Mod_Power()
# Body model import
kona_drivetrain = Mod_Body()
# Vehicle model import
kona_vehicle = Mod_Veh(kona_power, kona_drivetrain)
# Driver model import
drv_kyunghan = Mod_Driver()
# Behavior model import
beh_driving = Mod_Behavior(drv_kyunghan)
# Set model parameter
fcn_set_vehicle_param(kona_drivetrain, kona_vehicle, kona_param,
kona_param_est)
# RL controller
# Idm
cf_state_recog = DecelStateRecog()
idm_cls = IdmClassic()
# Agent
K.clear_session()
os.chdir(base_dir)
from pysim.models.model_vehicle import Mod_Veh, Mod_Body
from pysim.models.model_power import Mod_Power
## from models.model_maneuver import Mod_Behavior, Mod_Driver
## from models.model_environment import Mod_Env
from pysim.sub_util.sub_type_def import type_DataLog
#%% 1. Import models
# Powertrain import and configuration
kona_power = Mod_Power()
#%%
# ~~~~~
# Bodymodel import and configuration
kona_body = Mod_Body()
# ~~~~
# Vehicle set
kona_vehicle = Mod_Veh(kona_power, kona_body)
#%% 2. Simulation config
Ts = 0.01
sim_time = 40
sim_time_range = np.arange(0, sim_time, 0.01)
# ----------------------------- select input set ---------------------------
Input_index = 2
if Input_index == 0:
# Go straight : Input_index = 0
u_acc_val = np.concatenate(
(0 * np.ones(int(len(sim_time_range) * 0.1)),
0.3 * np.ones(int(len(sim_time_range) * 0.9))))
u_brk_val = 0 * np.ones(len(sim_time_range))
elif Input_index == 1: