pid

Torch-based PID controllers for batched simulation.

Classes:

Name	Description
PID	Vectorized PID controller for multiple environments.
FlightController	PID flight controller (batched).
TrackController	PID track controller (batched).

PID

PID(Kp: Tensor, Ki: Tensor | None = None, Kd: Tensor | None = None, dt: float = 0.01, N: float = 100.0, integral_limit: Tensor | None = None, num_envs: int = 1)

Vectorized PID controller for multiple environments.

Initialize PID gains and state tensors.

Parameters:

Name	Type	Description	Default
Kp	Tensor	Proportional gain tensor with a leading environment dimension.	required
Ki	Tensor | None	Integral gain tensor with the same shape as Kp.	None
Kd	Tensor | None	Derivative gain tensor with the same shape as Kp.	None
dt	float	Controller time step.	0.01
N	float	First-order derivative filter coefficient.	100.0
integral_limit	Tensor | None	Optional clamp applied elementwise to the integral state.	None
num_envs	int	Number of environments.	1

Methods:

Name	Description
reset	Reset PID state for selected environments.
update	Compute PID output for the given error tensor.

reset

reset(env_ids: slice | Sequence[int] | Tensor) -> None

Reset PID state for selected environments.

Parameters:

Name	Type	Description	Default
env_ids	slice | Sequence[int] | Tensor	Indices or slice selecting which environments to reset.	required

update

update(error: Tensor) -> torch.Tensor

Compute PID output for the given error tensor.

Parameters:

Name	Type	Description	Default
error	Tensor	Batched control error tensor compatible with the internal state shape determined from Kp.	required

Returns:

Type	Description
Tensor	torch.Tensor: Combined P+I+D control output.

FlightController

FlightController(params: VehicleParams = VehicleParams(), control_mask: dict | None = None, gains: dict | None = None, limits: dict | None = None, num_envs: int = 1, device: str | device = 'cpu')

Bases: ControllerBase

PID flight controller (batched).

Initialize the batched flight PID controller.

Methods:

Name	Description
randomize	Randomize configured PID gains, controller limits, and physical params.
reset	Reset PID loops for selected environments.
update	Update controller.
gains_to_randomization_ranges	Convert static gains dict to domain randomization min/max ranges.

Attributes:

Name	Type	Description
params	VehicleParams	Vehicle parameters for the controller.

params instance-attribute

params: VehicleParams = params

Vehicle parameters for the controller.

randomize

randomize(env_ids=None, gains=None, limits=None, params=None)

Randomize configured PID gains, controller limits, and physical params.

reset

reset(env_ids)

Reset PID loops for selected environments.

update

update(target: dict, state: dict) -> torch.Tensor

Update controller.

Parameters:

Name	Type	Description	Default
target	dict	dict with batched tensors, keys matching mask.	required
state	dict	dict: pos(N,3), vel(N,3), att_euler(N,3), ang_vel(N,3).	required

Returns:

Name	Type	Description
Tensor	(N, 4)	[thrust, roll_moment, pitch_moment, yaw_moment].

gains_to_randomization_ranges classmethod

gains_to_randomization_ranges(gains=None, scale=0.2)

Convert static gains dict to domain randomization min/max ranges.

Each non-zero gain is scaled by (1-scale) for min and (1+scale) for max. Defaults to cls.DEFAULT_GAINS when gains is None.

TrackController

TrackController(params: VehicleParams = VehicleParams(), control_mask: dict | None = None, gains: dict | None = None, num_envs: int = 1, device: str | device = 'cpu')

Bases: ControllerBase

PID track controller (batched).

Initialize the batched track controller.

Methods:

Name	Description
randomize	Randomize configured PID gains for all or selected environments.
reset	Reset PID loops for selected environments.
update	Update controller.

Attributes:

Name	Type	Description
params	VehicleParams	Vehicle parameters for the controller.

params instance-attribute

params: VehicleParams = params

Vehicle parameters for the controller.

randomize

randomize(env_ids=None, gains=None)

Randomize configured PID gains for all or selected environments.

reset

reset(env_ids)

Reset PID loops for selected environments.

update

update(target: dict, state: dict) -> torch.Tensor

Update controller.

Parameters:

Name	Type	Description	Default
target	dict	dict: pos(N,3), vel_b(N,3), att_euler(N,3), ang_vel(N,3). Only pos[:,:2], vel_b[:,0], att_euler[:,2], ang_vel[:,2] are used.	required
state	dict	dict with same keys.	required

Returns:

Name	Type	Description
Tensor	(N, 2)	left/right acceleration commands.