Control Models¶
-
class
stonesoup.models.control.base.
ControlModel
(ndim_state, mapping)[source]¶ Bases:
stonesoup.models.base.Model
Control Model base class
- Parameters
ndim_state (
int
) – Number of state dimensionsmapping (
Sequence[int]
) – Mapping between control and state dims
-
abstract property
ndim_ctrl
¶ Number of control input dimensions
-
class
stonesoup.models.control.linear.
LinearControlModel
(ndim_state, mapping, control_vector, control_matrix, control_noise=None)[source]¶ Bases:
stonesoup.models.control.base.ControlModel
,stonesoup.models.base.LinearModel
Implements a linear effect to the state vector via,
\[\hat{x}_k = B_k \mathbf{u}_k + \gamma_k\]where \(B_k\) is the control-input model matrix (i.e. control matrix), \(\mathbf{u}_k\) is the control vector and \(\gamma_k\) is sampled from zero-mean white noise distribution \(\mathcal{N}(0,\Gamma_k)\)
- Parameters
ndim_state (
int
) – Number of state dimensionsmapping (
Sequence[int]
) – Mapping between control and state dimscontrol_vector (
numpy.ndarray
) – Control vector at time \(k\)control_matrix (
numpy.ndarray
) – Control input model matrix at time \(k\), \(B_k\)control_noise (
numpy.ndarray
, optional) – Control input noise covariance at time \(k\)
-
control_vector
: numpy.ndarray¶ Control vector at time \(k\)
-
control_matrix
: numpy.ndarray¶ Control input model matrix at time \(k\), \(B_k\)
-
control_noise
: numpy.ndarray¶ Control input noise covariance at time \(k\)
-
property
ndim
¶ Number of dimensions of model
-
property
ndim_ctrl
¶ Number of control input dimensions
-
control_input
()[source]¶ The mean control input
- Returns
the noiseless effect of the control input, \(B_k \mathbf{u}_k\)
- Return type
-
rvs
()[source]¶ Sample (once) from the multivariate normal distribution determined from the mean and covariance control parameters
- Returns
a sample from \(\mathcal{N}(B_k \mathbf{u}_k, \Gamma_k)\)
- Return type
-
pdf
(control_vec)[source]¶ The value of the probability density function (pdf) at a test point
- Parameters
control_vec (
numpy.ndarray
) – The control vector at the test point- Returns
The value of the pdf at
control_vec
- Return type