def __init__(self, motor='DcPermEx', reward_function=None, reference_generator=None, **kwargs):
"""
Args:
motor(ElectricMotor): Electric Motor used in the PhysicalSystem
reward_function(RewardFunction): Reward Function for the environment
reference_generator(ReferenceGenerator): Reference Generator for the environment
kwargs(dict): Further kwargs tot pass to the superclass and the submodules
"""
physical_system = DcMotorSystem(motor=motor, **kwargs)
reference_generator = reference_generator or WienerProcessReferenceGenerator(**kwargs)
reward_function = reward_function or WeightedSumOfErrors(**kwargs)
super().__init__(
physical_system, reference_generator=reference_generator, reward_function=reward_function, **kwargs
)
def __init__(self, motor='SynRM', reward_function=None, reference_generator=None, constraints=None, **kwargs):
"""
Args:
motor(ElectricMotor): Electric Motor used in the PhysicalSystem
reward_function(RewardFunction): Reward Function for the environment
reference_generator(ReferenceGenerator): Reference Generator for the environment
kwargs(dict): Further kwargs tot pass to the superclass and the submodules
"""
physical_system = SynchronousMotorSystem(motor=motor, **kwargs)
reference_generator = reference_generator or WienerProcessReferenceGenerator(**kwargs)
reward_function = reward_function or WeightedSumOfErrors(**kwargs)
constraints_ = constraints if constraints is not None \
else ('i_a', 'i_b', 'i_c', SquaredConstraint(('i_sd', 'i_sq')))
super().__init__(
physical_system, reference_generator=reference_generator, reward_function=reward_function,
constraints=constraints_, **kwargs
)
motor_parameter=dict(r_a=15e-3, r_e=15e-3, l_a=1e-3, l_e=1e-3),
# Set the parameters of the mechanical polynomial load (the default load class)
load_parameter=dict(a=0, b=.1, c=.1, j_load=0.04),
# Defines the utilized power converter, which determines the action space
# 'Disc-1QC' is our notation for a discontinuous one-quadrant converter,
# which is a one-phase buck converter with available actions 'switch on' and 'switch off'
converter='Disc-1QC',
# Define which states will be shown in the state observation (what we can "measure")
state_filter=['omega', 'i'],
# Define the reward function and to which state variable it applies
# Here, we define it for current control
reward_function=WeightedSumOfErrors(observed_states='i'),
# Defines which numerical solver is to be used for the simulation
# euler is fastest but not most precise
ode_solver='euler',
solver_kwargs={},
# Define and parameterize the reference generator for the current reference
reference_generator=WienerProcessReferenceGenerator(
reference_state='i', sigma_range=(3e-3, 3e-2)),
# Defines which variables to plot via the builtin dashboard monitor
visualization=MotorDashboard(state_plots=['i', 'omega']),
)
# Now, the environment will output states and references separately