# Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from __future__ import annotations
import torch
from collections.abc import Sequence
from typing import TYPE_CHECKING
from omni.isaac.core.utils.types import ArticulationActions
from .actuator_base import ActuatorBase
if TYPE_CHECKING:
from .actuator_cfg import DCMotorCfg, IdealPDActuatorCfg, ImplicitActuatorCfg
"""
Implicit Actuator Models.
"""
[docs]class ImplicitActuator(ActuatorBase):
"""Implicit actuator model that is handled by the simulation.
This performs a similar function as the :class:`IdealPDActuator` class. However, the PD control is handled
implicitly by the simulation which performs continuous-time integration of the PD control law. This is
generally more accurate than the explicit PD control law used in :class:`IdealPDActuator` when the simulation
time-step is large.
.. note::
The articulation class sets the stiffness and damping parameters from the configuration into the simulation.
Thus, the parameters are not used in this class.
.. caution::
The class is only provided for consistency with the other actuator models. It does not implement any
functionality and should not be used. All values should be set to the simulation directly.
"""
cfg: ImplicitActuatorCfg
"""The configuration for the actuator model."""
"""
Operations.
"""
[docs] def reset(self, *args, **kwargs):
# This is a no-op. There is no state to reset for implicit actuators.
pass
[docs] def compute(
self, control_action: ArticulationActions, joint_pos: torch.Tensor, joint_vel: torch.Tensor
) -> ArticulationActions:
"""Compute the aproximmate torques for the actuated joint (physX does not compute this explicitly)."""
# store approximate torques for reward computation
error_pos = control_action.joint_positions - joint_pos
error_vel = control_action.joint_velocities - joint_vel
self.computed_effort = self.stiffness * error_pos + self.damping * error_vel + control_action.joint_efforts
# clip the torques based on the motor limits
self.applied_effort = self._clip_effort(self.computed_effort)
return control_action
"""
Explicit Actuator Models.
"""
[docs]class IdealPDActuator(ActuatorBase):
r"""Ideal torque-controlled actuator model with a simple saturation model.
It employs the following model for computing torques for the actuated joint :math:`j`:
.. math::
\tau_{j, computed} = k_p * (q - q_{des}) + k_d * (\dot{q} - \dot{q}_{des}) + \tau_{ff}
where, :math:`k_p` and :math:`k_d` are joint stiffness and damping gains, :math:`q` and :math:`\dot{q}`
are the current joint positions and velocities, :math:`q_{des}`, :math:`\dot{q}_{des}` and :math:`\tau_{ff}`
are the desired joint positions, velocities and torques commands.
The clipping model is based on the maximum torque applied by the motor. It is implemented as:
.. math::
\tau_{j, max} & = \gamma \times \tau_{motor, max} \\
\tau_{j, applied} & = clip(\tau_{computed}, -\tau_{j, max}, \tau_{j, max})
where the clipping function is defined as :math:`clip(x, x_{min}, x_{max}) = min(max(x, x_{min}), x_{max})`.
The parameters :math:`\gamma` is the gear ratio of the gear box connecting the motor and the actuated joint ends,
and :math:`\tau_{motor, max}` is the maximum motor effort possible. These parameters are read from
the configuration instance passed to the class.
"""
cfg: IdealPDActuatorCfg
"""The configuration for the actuator model."""
"""
Operations.
"""
[docs] def reset(self, env_ids: Sequence[int]):
pass
[docs] def compute(
self, control_action: ArticulationActions, joint_pos: torch.Tensor, joint_vel: torch.Tensor
) -> ArticulationActions:
# compute errors
error_pos = control_action.joint_positions - joint_pos
error_vel = control_action.joint_velocities - joint_vel
# calculate the desired joint torques
self.computed_effort = self.stiffness * error_pos + self.damping * error_vel + control_action.joint_efforts
# clip the torques based on the motor limits
self.applied_effort = self._clip_effort(self.computed_effort)
# set the computed actions back into the control action
control_action.joint_efforts = self.applied_effort
control_action.joint_positions = None
control_action.joint_velocities = None
return control_action
[docs]class DCMotor(IdealPDActuator):
r"""
Direct control (DC) motor actuator model with velocity-based saturation model.
It uses the same model as the :class:`IdealActuator` for computing the torques from input commands.
However, it implements a saturation model defined by DC motor characteristics.
A DC motor is a type of electric motor that is powered by direct current electricity. In most cases,
the motor is connected to a constant source of voltage supply, and the current is controlled by a rheostat.
Depending on various design factors such as windings and materials, the motor can draw a limited maximum power
from the electronic source, which limits the produced motor torque and speed.
A DC motor characteristics are defined by the following parameters:
* Continuous-rated speed (:math:`\dot{q}_{motor, max}`) : The maximum-rated speed of the motor.
* Continuous-stall torque (:math:`\tau_{motor, max}`): The maximum-rated torque produced at 0 speed.
* Saturation torque (:math:`\tau_{motor, sat}`): The maximum torque that can be outputted for a short period.
Based on these parameters, the instantaneous minimum and maximum torques are defined as follows:
.. math::
\tau_{j, max}(\dot{q}) & = clip \left (\tau_{j, sat} \times \left(1 -
\frac{\dot{q}}{\dot{q}_{j, max}}\right), 0.0, \tau_{j, max} \right) \\
\tau_{j, min}(\dot{q}) & = clip \left (\tau_{j, sat} \times \left( -1 -
\frac{\dot{q}}{\dot{q}_{j, max}}\right), - \tau_{j, max}, 0.0 \right)
where :math:`\gamma` is the gear ratio of the gear box connecting the motor and the actuated joint ends,
:math:`\dot{q}_{j, max} = \gamma^{-1} \times \dot{q}_{motor, max}`, :math:`\tau_{j, max} =
\gamma \times \tau_{motor, max}` and :math:`\tau_{j, peak} = \gamma \times \tau_{motor, peak}`
are the maximum joint velocity, maximum joint torque and peak torque, respectively. These parameters
are read from the configuration instance passed to the class.
Using these values, the computed torques are clipped to the minimum and maximum values based on the
instantaneous joint velocity:
.. math::
\tau_{j, applied} = clip(\tau_{computed}, \tau_{j, min}(\dot{q}), \tau_{j, max}(\dot{q}))
"""
cfg: DCMotorCfg
"""The configuration for the actuator model."""
[docs] def __init__(self, cfg: DCMotorCfg, *args, **kwargs):
super().__init__(cfg, *args, **kwargs)
# parse configuration
if self.cfg.saturation_effort is not None:
self._saturation_effort = self.cfg.saturation_effort
else:
self._saturation_effort = torch.inf
# prepare joint vel buffer for max effort computation
self._joint_vel = torch.zeros_like(self.computed_effort)
# create buffer for zeros effort
self._zeros_effort = torch.zeros_like(self.computed_effort)
# check that quantities are provided
if self.cfg.velocity_limit is None:
raise ValueError("The velocity limit must be provided for the DC motor actuator model.")
"""
Operations.
"""
[docs] def compute(
self, control_action: ArticulationActions, joint_pos: torch.Tensor, joint_vel: torch.Tensor
) -> ArticulationActions:
# save current joint vel
self._joint_vel[:] = joint_vel
# calculate the desired joint torques
return super().compute(control_action, joint_pos, joint_vel)
"""
Helper functions.
"""
def _clip_effort(self, effort: torch.Tensor) -> torch.Tensor:
# compute torque limits
# -- max limit
max_effort = self._saturation_effort * (1.0 - self._joint_vel / self.velocity_limit)
max_effort = torch.clip(max_effort, min=self._zeros_effort, max=self.effort_limit)
# -- min limit
min_effort = self._saturation_effort * (-1.0 - self._joint_vel / self.velocity_limit)
min_effort = torch.clip(min_effort, min=-self.effort_limit, max=self._zeros_effort)
# clip the torques based on the motor limits
return torch.clip(effort, min=min_effort, max=max_effort)
